Innodatainc

<div>Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.</div>About this Role:
Produce high-fidelity annotations on imagery and video used to train and evaluate machine learning models. Annotators work to a defined ontology and labeling specification, meet project quality and throughput targets, and operate inside the designated annotation environment for each program. Assignments rotate across projects, modalities, and customers as program needs change.
<h3>Key Responsibilities:</h3>
<ul>
<li>Label static imagery and video across a range of sensor types, image qualities, and scene conditions, including both real-world and synthetic data.</li>
<li>Produce 2D bounding boxes, instance and semantic segmentation masks, and keypoint annotations to project specification.</li>
<li>Produce oriented and rotated bounding boxes where scene geometry requires them.</li>
<li>Produce 3D and 6 degrees-of-freedom pose, orientation, and scale annotations on projects that require them.</li>
<li>Produce multi-object tracking annotations, maintaining consistent object identity across full sequences — through occlusion, frame exit and re-entry, scale change, and camera or platform motion.</li>
<li>Review, correct, and accept or reject model-assisted and pre-labeled output; report systematic pre-label failure modes rather than silently correcting the same error frame by frame.</li>
<li>Work strictly to the project ontology and guidelines; escalate ambiguous or out-of-ontology objects rather than guessing.</li>
<li>Meet assigned accuracy and throughput targets, and hold that standard consistently across large batches.</li>
<li>Complete rework promptly from QA feedback, applying the correction to comparable cases in the same batch.</li>
<li>Log edge cases and recurring ambiguities so they can be adjudicated and folded into the guidelines.</li>
<li>Follow all customer data-handling, confidentiality, and information security requirements for the assigned project, and keep required training current. </li>
</ul>
<h3>Must-Have Qualifications: </h3>
<ul>
<li>1+ years of image or video annotation experience, or equivalent precision work in a quality-managed production environment.</li>
<li>Working familiarity with at least one professional annotation platform (for example CVAT, V7 Darwin, Labelbox, Scale, or comparable).</li>
<li>Practical understanding of bounding boxes, segmentation masks, keypoints, and object tracking — and of what makes each one correct rather than merely present.</li>
<li>Strong visual attention to detail and the discipline to hold a standard across tens of thousands of frames.</li>
<li>Ability to follow written labeling guidelines exactly, and to ask precise questions when they are silent on a case.</li>
<li>Comfortable working in remote-desktop or browser-based environments.</li>
</ul>
Nice-to-Have Qualifications:
<ul>
<li>Experience across multiple annotation modalities rather than a single task type.</li>
<li>Experience with aerial, overhead, satellite, or thermal/IR imagery.</li>
<li>Experience with long-form video and tracking work.</li>
<li>Experience reviewing model-assisted pre-labels in a human-in-the-loop pipeline.</li>
<li>Prior work on government or regulated-industry programs.</li>
</ul>
The expected hourly salary range for this position is $42,000 to $62.500 annually, based on experience, skills, and qualifications.
Program eligibility: Some programs require eligibility for a government background investigation or credentialing. Assignment to those programs is contingent on meeting those requirements.<div>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Data Annotator

<div>Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.</div>Scope of the Role: 
We are hiring a Staff / Lead-level Data Analyst / Analytics Engineer to embed with the Monetization Data Science &amp; Analytics team as a senior individual contributor and technical leader. This person will be the go-to analytics expert for advertiser revenue, monetization performance, and growth metrics — trusted by Data Scientists, Analysts, PMs, and Engineering leaders to drive high-impact work end-to-end.
This is a staff-level Individual Contributor role, not a mid-level execution seat. The successful candidate operates as:
<ul>
<li>A trusted thought partner to Data Scientists and Product leaders — someone who improves the quality of the question before answering it.</li>
<li>A technical leader who sets standards for data models, pipelines, and dashboards that others follow.</li>
<li>A force multiplier who unblocks the team by identifying and fixing root causes across the data stack, not just building what's asked.</li>
</ul>
What You’ll Own:
<ul>
<li>50% — Analytics, Business Insights &amp; Technical Leadership Partnering with Data Scientists and Product on the hardest analytics problems; driving metric definitions; reviewing others' analyses; setting standards for the team's analytics work.</li>
<li>30% — Data Engineering &amp; Pipeline Ownership Architecting and owning production SQL pipelines, data models, and data cubes; designing and operating Airflow DAGs; setting the bar for data quality, reliability, and reconciliation across the domain.</li>
<li>20% — Data Visualization, Metric Governance &amp; Enablement Owning executive-visibility dashboards in Tableau / Superset; defining and governing metrics; enabling self-serve analytics for the broader Monetization org.</li>
</ul>
<h3>Analytics Leadership &amp; Business Partnership</h3>
<ul>
<li>Serve as the senior analytics IC for the Monetization Analytics pod — the person Data Scientists and PMs come to with the hardest, most ambiguous data problems.</li>
<li>Improve the quality of the question before answering — reframe vague asks into sharper, more valuable analytical approaches.</li>
<li>Lead end-to-end analytics initiatives that span data modeling, pipeline work, and dashboard delivery — with minimal supervision and clear stakeholder communication throughout.</li>
<li>Set metric definitions and standards for advertiser revenue, monetization performance, funnel/cohort metrics, and experiment readouts — and drive consistency across dashboards.</li>
<li>Independently drive root-cause analysis on data discrepancies across dashboards, warehouses, or pipelines — including cross-team debugging when needed.</li>
<li>Review, coach, and raise the bar on the work of other analysts and analytics engineers on the team.</li>
</ul>
<h3>Data Engineering &amp; Pipeline Architecture</h3>
<ul>
<li>Architect and own production-grade SQL data pipelines (Presto / Trino / Hive / Spark SQL) — including making the right tradeoffs on incremental vs. full refresh, pre-aggregation, and cost/performance.</li>
<li>Design and own data cubes, aggregate tables, and semantic layers used by the whole Monetization Analytics function.</li>
<li>Author, own, and operate Airflow DAGs for critical revenue and monetization pipelines — including SLAs, on-call posture, backfills, and incident response.</li>
<li>Set and enforce standards for data quality, reconciliation, and observability — row counts, revenue tie-outs, distribution checks, anomaly alerting — across the domain.</li>
<li>Optimize existing pipelines aggressively for cost and latency (partitioning, incremental refresh, query tuning on billion+ row tables) — and quantify the wins.</li>
<li>Contribute to cross-team technical decisions — table designs, upstream schema changes, migration plans (e.g., Hive → Trino) — via design docs and reviews.</li>
</ul>
<h3>Data Visualization, Metric Governance &amp; Enablement</h3>
<ul>
<li>Own the design and quality of executive and cross-functional dashboards in Tableau and/or Superset.</li>
<li>Drive metric governance — clear definitions, owners, source-of-truth queries, validation, deprecation.</li>
<li>Enable self-serve analytics for Data Scientists, Analysts, and PMs — clear naming, documentation, certified metrics, sensible defaults, and coaching.</li>
</ul>
You’ll Thrive in This Role If You Have:
<ul>
<li>9+ years of combined experience in Data Analytics, Business Intelligence, or Analytics Engineering roles.</li>
<li>At least 3+ years at Senior level or above in an analytics-adjacent role at a high-scale tech, ads-tech, marketplace, or fintech company.</li>
<li>Prior experience as the most senior analytics IC on an embedded team — or a strong case for why they're ready to step into that role now.</li>
<li>Track record of leading end-to-end analytics initiatives — from ambiguous business question through data model, pipeline, dashboard, and rollout.</li>
<li>Prior experience partnering directly with US-based Data Science, Product, and Engineering leaders as a full contributor.</li>
</ul>
<h3>Technical Skills — SQL &amp; Data Engineering (Advanced)</h3>
<ul>
<li>Expert-level SQL — deep proficiency with window functions, CTEs, complex joins, query optimization, incremental patterns, skew mitigation, and cost tuning on billion+ row tables.</li>
<li>Deep hands-on with at least two of: Presto, Trino, Hive, Spark SQL, Snowflake, BigQuery, Redshift. Can reason about query plans and physical layout, not just syntax.</li>
<li>Advanced Airflow — has architected and operated large DAG ecosystems (50+ production DAGs), including cross-DAG dependencies, backfills at scale, and SLA management. Equivalent orchestrators (Dagster, Prefect) also acceptable if depth is comparable.</li>
<li>Data architecture &amp; modeling depth — Kimball, star schema, dimensional modeling, OLAP cubes, wide fact tables, slowly-changing dimensions, semantic layer design. Can defend design tradeoffs in a design review.</li>
<li>ETL / ELT architecture — incremental loads, backfills, idempotency, data quality frameworks, lineage.</li>
<li>Python for data work — pandas, PySpark, scripting, and light tooling development.</li>
<li>dbt or equivalent transformation framework experience strongly preferred.</li>
<li>Experience contributing to or reviewing design docs and RFCs for data platforms and pipelines.</li>
</ul>
<h3>Technical Skills — Visualization</h3>
<ul>
<li>Deep, hands-on production experience building executive-grade dashboards in Tableau and/or Apache Superset (Looker, Power BI, Mode also acceptable).</li>
<li>Strong opinions on dashboard design — headline vs. drilldown metrics, layout, filters, performance, self-serve UX.</li>
<li>Experience driving metric governance and self-serve BI at an org level.</li>
</ul>
<h3>Analytics &amp; Business Skills</h3>
<ul>
<li>Strong grasp of KPI definition, metric design, funnel analysis, cohort analysis, and A/B testing methodology.</li>
<li>Deep exposure to digital advertising / monetization metrics — impressions, clicks, CTR, CPM, CPC, CVR, ROAS, revenue attribution, incrementality — is strongly preferred.</li>
<li>Prior experience at ads-tech, digital media, or major consumer/marketplace tech companies (Meta, Google, Amazon, Uber, DoorDash, Snap, TikTok, LinkedIn, Airbnb, Instacart, Pinterest peers, etc.) is a strong plus.</li>
<li>Comfort reading experiment results and challenging methodology when needed.</li>
</ul>
<h3>Leadership, Communication &amp; Ways of Working</h3>
<ul>
<li>Native or near-native English (spoken and written) — this is a hard requirement.</li>
<li>Track record of leading initiatives end-to-end with minimal direction — scoping, aligning stakeholders, executing, and communicating results.</li>
<li>Comfortable pushing back on unclear or misdirected requirements and proposing better approaches.</li>
<li>Prolific writer of design docs, RFCs, requirement docs, and postmortems.</li>
<li>Experience mentoring or coaching less-senior analysts and analytics engineers — even if not a formal manager.</li>
<li>Executive presence — can present analytics work to Director/VP-level stakeholders and defend recommendations.</li>
<li>Operates with the ownership mindset of a permanent employee, even in a contract role.</li>
</ul>
 
The expected salary range for this position is $130,000 – $150,000 USD per year, based on experience, skills, and qualifications.
 
 <div>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Lead Analytics Engineer

<div>Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.</div>Scope of the Role: 
We are hiring a Staff / Lead-level Data Analyst / Analytics Engineer to embed with the Monetization Data Science &amp; Analytics team as a senior individual contributor and technical leader. This person will be the go-to analytics expert for advertiser revenue, monetization performance, and growth metrics — trusted by Data Scientists, Analysts, PMs, and Engineering leaders to drive high-impact work end-to-end.
This is a staff-level Individual Contributor role, not a mid-level execution seat. The successful candidate operates as:
<ul>
<li>A trusted thought partner to Data Scientists and Product leaders — someone who improves the quality of the question before answering it.</li>
<li>A technical leader who sets standards for data models, pipelines, and dashboards that others follow.</li>
<li>A force multiplier who unblocks the team by identifying and fixing root causes across the data stack, not just building what's asked.</li>
</ul>
What You’ll Own:
<ul>
<li>50% — Analytics, Business Insights &amp; Technical Leadership Partnering with Data Scientists and Product on the hardest analytics problems; driving metric definitions; reviewing others' analyses; setting standards for the team's analytics work.</li>
<li>30% — Data Engineering &amp; Pipeline Ownership Architecting and owning production SQL pipelines, data models, and data cubes; designing and operating Airflow DAGs; setting the bar for data quality, reliability, and reconciliation across the domain.</li>
<li>20% — Data Visualization, Metric Governance &amp; Enablement Owning executive-visibility dashboards in Tableau / Superset; defining and governing metrics; enabling self-serve analytics for the broader Monetization org.</li>
</ul>
<h3>Analytics Leadership &amp; Business Partnership</h3>
<ul>
<li>Serve as the senior analytics IC for the Monetization Analytics pod — the person Data Scientists and PMs come to with the hardest, most ambiguous data problems.</li>
<li>Improve the quality of the question before answering — reframe vague asks into sharper, more valuable analytical approaches.</li>
<li>Lead end-to-end analytics initiatives that span data modeling, pipeline work, and dashboard delivery — with minimal supervision and clear stakeholder communication throughout.</li>
<li>Set metric definitions and standards for advertiser revenue, monetization performance, funnel/cohort metrics, and experiment readouts — and drive consistency across dashboards.</li>
<li>Independently drive root-cause analysis on data discrepancies across dashboards, warehouses, or pipelines — including cross-team debugging when needed.</li>
<li>Review, coach, and raise the bar on the work of other analysts and analytics engineers on the team.</li>
</ul>
<h3>Data Engineering &amp; Pipeline Architecture</h3>
<ul>
<li>Architect and own production-grade SQL data pipelines (Presto / Trino / Hive / Spark SQL) — including making the right tradeoffs on incremental vs. full refresh, pre-aggregation, and cost/performance.</li>
<li>Design and own data cubes, aggregate tables, and semantic layers used by the whole Monetization Analytics function.</li>
<li>Author, own, and operate Airflow DAGs for critical revenue and monetization pipelines — including SLAs, on-call posture, backfills, and incident response.</li>
<li>Set and enforce standards for data quality, reconciliation, and observability — row counts, revenue tie-outs, distribution checks, anomaly alerting — across the domain.</li>
<li>Optimize existing pipelines aggressively for cost and latency (partitioning, incremental refresh, query tuning on billion+ row tables) — and quantify the wins.</li>
<li>Contribute to cross-team technical decisions — table designs, upstream schema changes, migration plans (e.g., Hive → Trino) — via design docs and reviews.</li>
</ul>
<h3>Data Visualization, Metric Governance &amp; Enablement</h3>
<ul>
<li>Own the design and quality of executive and cross-functional dashboards in Tableau and/or Superset.</li>
<li>Drive metric governance — clear definitions, owners, source-of-truth queries, validation, deprecation.</li>
<li>Enable self-serve analytics for Data Scientists, Analysts, and PMs — clear naming, documentation, certified metrics, sensible defaults, and coaching.</li>
</ul>
You’ll Thrive in This Role If You Have:
<ul>
<li>9+ years of combined experience in Data Analytics, Business Intelligence, or Analytics Engineering roles.</li>
<li>At least 3+ years at Senior level or above in an analytics-adjacent role at a high-scale tech, ads-tech, marketplace, or fintech company.</li>
<li>Prior experience as the most senior analytics IC on an embedded team — or a strong case for why they're ready to step into that role now.</li>
<li>Track record of leading end-to-end analytics initiatives — from ambiguous business question through data model, pipeline, dashboard, and rollout.</li>
<li>Prior experience partnering directly with US-based Data Science, Product, and Engineering leaders as a full contributor.</li>
</ul>
<h3>Technical Skills — SQL &amp; Data Engineering (Advanced)</h3>
<ul>
<li>Expert-level SQL — deep proficiency with window functions, CTEs, complex joins, query optimization, incremental patterns, skew mitigation, and cost tuning on billion+ row tables.</li>
<li>Deep hands-on with at least two of: Presto, Trino, Hive, Spark SQL, Snowflake, BigQuery, Redshift. Can reason about query plans and physical layout, not just syntax.</li>
<li>Advanced Airflow — has architected and operated large DAG ecosystems (50+ production DAGs), including cross-DAG dependencies, backfills at scale, and SLA management. Equivalent orchestrators (Dagster, Prefect) also acceptable if depth is comparable.</li>
<li>Data architecture &amp; modeling depth — Kimball, star schema, dimensional modeling, OLAP cubes, wide fact tables, slowly-changing dimensions, semantic layer design. Can defend design tradeoffs in a design review.</li>
<li>ETL / ELT architecture — incremental loads, backfills, idempotency, data quality frameworks, lineage.</li>
<li>Python for data work — pandas, PySpark, scripting, and light tooling development.</li>
<li>dbt or equivalent transformation framework experience strongly preferred.</li>
<li>Experience contributing to or reviewing design docs and RFCs for data platforms and pipelines.</li>
</ul>
<h3>Technical Skills — Visualization</h3>
<ul>
<li>Deep, hands-on production experience building executive-grade dashboards in Tableau and/or Apache Superset (Looker, Power BI, Mode also acceptable).</li>
<li>Strong opinions on dashboard design — headline vs. drilldown metrics, layout, filters, performance, self-serve UX.</li>
<li>Experience driving metric governance and self-serve BI at an org level.</li>
</ul>
<h3>Analytics &amp; Business Skills</h3>
<ul>
<li>Strong grasp of KPI definition, metric design, funnel analysis, cohort analysis, and A/B testing methodology.</li>
<li>Deep exposure to digital advertising / monetization metrics — impressions, clicks, CTR, CPM, CPC, CVR, ROAS, revenue attribution, incrementality — is strongly preferred.</li>
<li>Prior experience at ads-tech, digital media, or major consumer/marketplace tech companies (Meta, Google, Amazon, Uber, DoorDash, Snap, TikTok, LinkedIn, Airbnb, Instacart, Pinterest peers, etc.) is a strong plus.</li>
<li>Comfort reading experiment results and challenging methodology when needed.</li>
</ul>
<h3>Leadership, Communication &amp; Ways of Working</h3>
<ul>
<li>Native or near-native English (spoken and written) — this is a hard requirement.</li>
<li>Track record of leading initiatives end-to-end with minimal direction — scoping, aligning stakeholders, executing, and communicating results.</li>
<li>Comfortable pushing back on unclear or misdirected requirements and proposing better approaches.</li>
<li>Prolific writer of design docs, RFCs, requirement docs, and postmortems.</li>
<li>Experience mentoring or coaching less-senior analysts and analytics engineers — even if not a formal manager.</li>
<li>Executive presence — can present analytics work to Director/VP-level stakeholders and defend recommendations.</li>
<li>Operates with the ownership mindset of a permanent employee, even in a contract role.</li>
</ul>
 
The expected salary range for this position is $130,000 – $150,000 CAD per year, based on experience, skills, and qualifications.
 <div>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

<div>Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.</div>
Scope of the Role: 
Innodata is expanding its GenAI research capability to advance state-of-the-art evaluation and post-training methods for LLM and multimodal systems. As an Applied Research Scientist, LLM Evaluation &amp; Post-Training, you will lead research and experimentation on how evaluation design, measurement strategies, and feedback signals influence model improvement.
This role is ideal for a technically rigorous researcher who is deeply fluent in modern LLM evaluation and post-training, and who can turn research insight into practical methods for customer solutions and internal platform innovation. You will work across human-in-the-loop and AI-augmented workflows, partnering with Language Data Scientists and AI/ML Research Engineers to design and validate evaluation frameworks that drive measurable model gains.
The ideal candidate combines strong experimental and statistical judgment with hands-on technical ability and can engage as a peer with research and engineering stakeholders at leading AI companies.
What You’ll Own:
As an Applied Research Scientist, LLM Evaluation &amp; Post-Training, you will help define the next generation of evaluation-driven model improvement workflows. You will study how different evaluation approaches (human, automated, hybrid) shape model selection and post-training outcomes, and you will design experiments that produce credible, actionable conclusions.
Your work may include designing benchmark datasets, developing evaluation taxonomies and protocols, defining metrics and scoring methodologies, analyzing failure modes, and testing how changes in evaluation setup affect downstream fine-tuning results. You will also support customer engagements by bringing scientific rigor to evaluation strategy, methodology review, and technical recommendations.
This is a highly collaborative role that sits at the intersection of research, engineering, and language/data operations. Additional responsibilities include (but are not limited to):
<ul>
<li>Define and execute a research agenda focused on LLM evaluation and post-training, especially evaluation-driven model improvement</li>
<li>Design rigorous experiments to study how evaluation methodologies impact fine-tuning and post-training outcomes</li>
<li>Develop and validate evaluation frameworks for LLM and multimodal systems, including:</li>
<ul>
<li>benchmark/task design</li>
<li>scoring methods</li>
<li>judge/model-assisted evaluation</li>
<li>human evaluation protocols</li>
<li>robustness/stress testing</li>
</ul>
<li>Lead research on advanced evaluation domains, including long-context, cross-modal, and dynamic multi-turn evaluations</li>
<li>Study the effectiveness and limitations of existing evaluation techniques, and propose improved methodologies with clear validity and scalability tradeoffs</li>
<li>Analyze model behavior and failure patterns; generate actionable recommendations for model improvement and evaluation redesign</li>
<li>Collaborate with AI/ML Research Engineers to translate research methods into scalable evaluation and post-training pipelines</li>
<li>Collaborate with Language Data Scientists to integrate human-in-the-loop and synthetic data/evaluation strategies into research programs</li>
<li>Engage with customer technical stakeholders to understand evaluation goals, review methodologies, and provide expert recommendations</li>
<li>Contribute to internal benchmark datasets, evaluation frameworks, and reusable research assets</li>
<li>Produce high-quality technical documentation, internal research reports, and client-facing materials explaining methods, results, assumptions, and limitations</li>
<li>Contribute to thought leadership and best practices in LLM evaluation, post-training, and GenAI quality measurement</li>
</ul>
You’ll Thrive in This Role If You Have:
<ul>
<li>MS/PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, AI, or a related quantitative scientific field (PhD strongly preferred)</li>
<li>5+ years of relevant experience in applied research / research science in ML/AI, with substantial work in LLMs or foundation models</li>
<li>Demonstrated experience with LLM evaluation, benchmarking, alignment, post-training, or model quality research</li>
<li>Strong foundation in experimental design, statistical analysis, and scientific reasoning for ML systems</li>
<li>Strong coding skills in Python for research experimentation and analysis (e.g., data processing, evaluation pipelines, statistical analysis, visualization)</li>
<li>Experience working with modern ML tooling/frameworks (e.g., PyTorch, Hugging Face, JAX/TensorFlow as applicable) sufficient to design and execute model/evaluation experiments</li>
<li>Ability to evaluate and compare human and automated evaluation methods, including tradeoffs in cost, reliability, validity, and scalability</li>
<li>Experience designing evaluation studies and protocols that are reproducible across datasets, model versions, and evaluation runs</li>
<li>Ability to collaborate directly with technical stakeholders including research scientists, ML engineers, data scientists, and customer technical counterparts</li>
<li>Strong communication skills and ability to present nuanced technical conclusions, assumptions, and limitations clearly</li>
</ul>
The expected salary range for this position is $245,000 – $315,000 CAD per year, based on experience, skills, and qualifications.
 
<div>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Applied Research Scientist, LLM Evaluation & Post-Training

<div>Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.</div> 
Scope of the Role: 
Financial services is one of the highest-stakes domains for generative AI. Numerical accuracy, regulatory compliance, model risk management, auditability, and customer harm prevention, among other concerns, are the bar for shipping anything real. Innodata partners with foundation model labs, banks, asset managers, fintechs, and other enterprise AI teams building LLMs, multimodal systems, and AI agents for financial workflows. 
As an Applied Data Scientist, Financial AI Evaluation &amp; Datasets, you own the design, measurement quality, and domain validity of the datasets used to train, fine-tune, evaluate, and monitor financial-domain LLMs, vision-language models, multimodal document models, and AI agents. You bring financial-domain fluency and data science rigor: you can read a risk policy, financial statement, or customer transcript, among other financial-services documents; turn it into a measurable dataset and evaluation specification; define what correct, grounded, compliant, and safe mean for the use case; and produce evidence that sophisticated financial-services customers, model-risk teams, and AI governance stakeholders can trust. 
This role has a special emphasis on unstructured and multimodal financial data — PDFs, scanned documents, spreadsheets, charts, call transcripts, and other mixed-document workflows where text, numbers, visuals, and metadata all matter. You will work in a pod with a Technical Solutions Architect (scopes the engagement), an Applied Research Scientist (shapes evaluation methodology), an AI/ML Research Engineer (builds training and evaluation infrastructure), and Language Data Scientists (run annotation at scale), making sure what the team produces is domain-valid, statistically defensible, compliant, auditable, and useful for evaluation and post-training. 
What You’ll Own:
<ul>
<li>Translate customer goals — such as improving financial reasoning, building an eval suite for earnings-call summarization, or evaluating an AML/fraud copilot — into concrete dataset specifications, taxonomies, rubrics, and acceptance criteria.</li>
<li>Design training and evaluation datasets across the financial AI surface: financial QA, filings and earnings analysis, credit and underwriting, fraud/AML investigation, and compliance, among other financial workflows.</li>
<li>Foreground unstructured and multimodal financial data in dataset design — PDFs, scanned statements, tables, charts, and call transcripts — used by analysts, advisors, compliance reviewers, and operations teams.</li>
<li>Design datasets and evaluations for retrieval-augmented and source-grounded systems: evidence citation and faithfulness to source documents, data freshness, conflict resolution across sources, and failure modes caused by incomplete or incorrectly parsed context.</li>
<li>Evaluate agentic and workflow-integrated financial AI systems: tool use, retrieval, transaction boundaries, escalation behavior, and controls that prevent unsafe or unauthorized actions.</li>
<li>Develop evaluation methodology that goes beyond surface accuracy — numerical consistency, hallucination rates on high-risk claims, refusal and escalation appropriateness, robustness under ambiguity, and fairness across protected or sensitive customer segments.</li>
<li>Define sampling strategies, label schemas, and adjudication workflows with Language Data Scientists and finance SMEs; write annotation guidelines that make subjective finance-domain judgments explicit, calibratable, and auditable.</li>
<li>Build the statistical and ML tooling that makes large financial datasets trustworthy: stratified sampling across products, markets, and modalities; bias analysis; leakage detection; and distribution shift checks, among other reliability checks.</li>
<li>Build evaluation and dataset-quality evidence to support financial-services model risk management: assumptions, limitations, validation results, and residual risks, packaged as reproducible evidence.</li>
<li>Partner with the AI/ML Research Engineer to instrument datasets into training, evaluation, and monitoring pipelines — rubric-grounded LLM-as-judge prompts, regression suites, and continuous monitoring.</li>
<li>Own data quality end-to-end, from intake through delivery: PII handling, provenance tracking, versioning, and modality-specific QA checks.</li>
<li>Reason about financial workflow context: where AI outputs enter analyst, advisor, compliance, risk, or customer-facing workflows; what evidence a reviewer needs to trust them; and when uncertainty must be surfaced.</li>
<li>Support the Technical Solutions Architect during customer discovery and proposals: scoping dataset programs, sizing annotation effort, and explaining methodology to client stakeholders.</li>
<li>Stay current on the financial AI landscape: regulatory developments, benchmark releases, and emerging evaluation methodology for finance-domain models.</li>
<li>Contribute to Innodata internal IP: reusable taxonomies, evaluation rubrics, golden datasets, and methodology templates. </li>
</ul>
You’ll Thrive in This Role If You Have:
<ul>
<li>5+ years of data science experience, with at least 2+ years in financial services, fintech, banking, or a comparable regulated data environment.</li>
<li>Real working knowledge of financial data and workflows: financial statements, SEC filings, transaction data, and other common financial-services document types.</li>
<li>Hands-on experience with unstructured and multimodal financial data — some combination of PDFs, scanned documents, spreadsheets, charts, or call transcripts.</li>
<li>Familiarity with financial standards or protocols such as XBRL, ISO 20022, or GAAP/IFRS reporting concepts, etc. is strongly preferred.</li>
<li>Hands-on experience designing datasets for ML — not just consuming them. You have written annotation guidelines, sized cohorts, set quality thresholds, and shipped data that downstream teams could actually train, evaluate, or monitor on.</li>
<li>Familiarity with LLM-based and multimodal financial AI workflows: prompt design, rubric-based evaluation, RAG, LLM-as-judge methods, and the limitations of automated evaluation in high-stakes contexts.</li>
<li>Strong Python and SQL; comfort with pandas, scikit-learn, or equivalent; working familiarity with Hugging Face, PyTorch, or model APIs.</li>
<li>Statistical literacy: sampling design, inter-annotator agreement metrics (e.g., Cohen's kappa), confidence intervals, and the ability to push back when a number is being over-interpreted.</li>
<li>Solid grasp of financial services privacy, compliance, and governance: PII handling, GLBA or equivalent privacy regimes, MNPI sensitivity, and documentation fit for regulated AI programs.</li>
<li>Excellent collaboration skills — upstream with a Technical Solutions Architect, sideways with research scientists and engineers, and downstream with SME annotators and quality teams.</li>
<li>A bias toward financial workflow realism. You would rather build a smaller dataset that reflects what analysts, advisors, or customers actually see than a larger one that looks impressive on paper but fails in practice.</li>
<li>Degree in a relevant field — statistics, data science, economics, finance, or a related quantitative field, or equivalent demonstrated experience. Formal finance credentials aren't required, but CFA, FRM, or MBA backgrounds, etc. are especially encouraged.</li>
<li>Experience designing evaluations for LLMs, VLMs, or multimodal models in financial reasoning, filings analysis, or fraud/AML contexts.</li>
<li>Experience with document AI, OCR/post-OCR quality, or table and chart extraction for complex financial documents.</li>
<li>Familiarity with agentic evaluation, AI observability, experiment tracking, or tools such as Weights &amp; Biases or LangFuse.</li>
<li>Familiarity with model risk management frameworks, validation documentation, fairness/bias auditing, or consumer protection analysis.</li>
<li>Experience with multilingual or cross-border financial data, or published/open-source work in financial AI or model governance. </li>
</ul>
The expected salary range for this position is $210,000 – $245,000 CAD per year, based on experience, skills, and qualifications.
 
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</div>

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