Gradient AI

**This is a fully remote position. 
Gradient AI: 
Gradient AI is a leading provider of AI solutions for the Group Health and P&amp;C insurance industries. Our solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Gradient AI's SaaS platform leverages a vast industry data lake comprising tens of millions of policies and claims, providing insurers with high resolution, data-driven insights. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. Founded in 2018, Gradient has experienced strong growth every year, and recently raised $56 million in Series C funding from top Insurtech investors. 
About the role: 
We are looking for a detail-oriented and creative thinker to join our team as a Senior QA Data Engineer. In this role, you will establish the next generation of data quality and testing at Gradient AI. The successful candidate will be responsible for ensuring the quality and trustworthiness of the customer data that powers our products by designing, building, and running automated data quality checks across our data pipeline. This individual will collaborate with engineering and customer-facing teams to catch data issues before they reach production. The ideal candidate is meticulous, comfortable working in an agile environment, a team player, a strong communicator, and ready to hit the ground running at a fast-growing AI startup. 
How will you make an impact: 
<ul>
<li>Build and maintain an automated data quality suite that runs on data refreshes through Airflow, replacing manual, per-customer validation. </li>
<li>Define checks as code (dbt tests, Soda, custom SQL) managed through version control and CI, including baseline checks so that new pipelines get coverage by default. </li>
<li>Validate loads for completeness, sequencing, and schema before processing, and halt or quarantine failures. </li>
<li>Reconcile data across Databricks layers and against downstream outputs such as pricing tools, at the portfolio, group, member, and claim level, using tolerance thresholds. </li>
<li>Detect anomalies, edge cases, and unexpected changes in historical data between loads. </li>
<li>Define severity levels and per-customer overrides and produce a data quality report for each customer that gates promotion to downstream consumers. </li>
<li>Track quality metrics over time to retire flaky checks and close coverage gaps. </li>
<li>Partner with engineers so that new pipelines ship with checks built in, and give engineers, stakeholders, and project managers timely feedback on quality issues and risk. </li>
</ul>
Skills needed to succeed: 
<ul>
<li>5+ years of professional experience in quality assurance with a strong focus on backend automated testing. </li>
<li>Hands-on experience building automated data tests with Soda, dbt, or similar frameworks. </li>
<li>Strong SQL skills (Spark SQL a plus) and familiarity with Python. </li>
<li>Experience with Airflow or similar orchestration tools, and with Databricks or a similar lakehouse environment (Delta Lake). </li>
<li>Familiarity with version control and CI/CD. </li>
<li>Solid understanding of testing principles, including functional, integration, and regression testing, applied to data pipelines. </li>
<li>Excellent analytical, debugging, and root-cause skills. </li>
<li>Strong verbal and written communication skills, including clear defect reports and quality summaries for technical and non-technical audiences. </li>
<li>Ability to work independently and coach others on testing best practices. </li>
<li>Bonus: Experience with health insurance or healthcare data, anomaly detection or data observability tools, or front-end test automation tools such as Cypress or Playwright. </li>
</ul>
Tools we use: dbt, Soda, SQL (Spark SQL), Python, Airflow, Databricks (Delta Lake) 
What we offer: 
<ul>
<li>A fun, team-oriented startup culture. </li>
<li>Generous stock options - we all get to own a piece of what we're building. </li>
<li>Unlimited vacation days. </li>
<li>Flexible schedule that supports working from home. </li>
<li>Full benefits package including medical, dental, vision, 401k, and more. </li>
<li>Ample opportunities to learn and take on new responsibilities. </li>
</ul>
We are an equal opportunity employer. 
Salary Range: $130,000-150,000 base salary annually. 
This role is also eligible for an annual performance bonus, equity grant, and a comprehensive benefits package. In accordance with the Massachusetts Pay Transparency Law, we are providing a good-faith salary range for this position at the time of posting. The actual salary offered will depend on the level at which the candidate is hired, as well as their experience, skills, qualifications, and location. Compensation may grow over time through merit-based increases, promotions, and company-wide adjustments. If your salary expectations fall outside this range, we still encourage you to apply so we can have a conversation.

Senior QA Data Engineer

This is a fully remote opportunity with hybrid available to those local to Boston.
Gradient AI:    
Gradient AI is the decision-intelligence partner for the insurance industry, giving customers an advantage in how they make decisions by revealing risk others miss and translating it into stronger performance and real-world impact. Our platform harnesses a vast industry data lake – tens of millions of policies and claims enriched with economic, health, geographic, and demographic signals – integrating cleanly with existing workflows to make complex risk clear, usable, and actionable. Our customers include carriers, brokers, consultants, and specialized insurance organizations across the industry.  We are backed by $56M in Series C funding and scaling fast – and it's an exciting time to join the team! 
About the Role:    
We are looking for a Staff Data Scientist to lead complex modelling and analytical projects of broad and diverse scope. You will drive cross-product initiatives with minimal direction, setting the technical approach where problems are ambiguous and raising the bar for the entire data science team. 
How you will make an impact: 
<ul>
<li>Leverage the best of modern deep learning &amp; large language models with traditional data science techniques to create powerful hybrid models with real uplift. </li>
<li>Brainstorm, prototype, prove, deploy, and realize the value of your work in market quickly. </li>
<li>Everything you would expect on a world-class data science team solving world-class problems.  Big data.  Federated learning.  Unstructured data challenges.  Timeseries and sequence modelling.  A self-serve buffet of techniques from GLMs to XGBoost to Transformers. </li>
<li>Tell stories with your data.  Inspire trust in customers, stakeholders, and prospects by turning murky math into a powerful message that drives the bottom line. </li>
</ul>
Who you are and why we want to work with you:
<ul>
<li>You like getting things over the line.  You have an insatiable desire to deliver value now and improve next.  MVP perfection is achieved not when there is nothing more to add, but when there is nothing left to take away. </li>
<li>You are not a software engineer, but you give them a run for their money.  You prefer Python to R and don’t understand why there is still a debate.  Jupyter is a necessary evil, and you’ve never met a command line that scared you away. </li>
<li>You still do a better job than Claude, and you’re skeptical of your friends who say they never code any more. </li>
<li>You love to take initiative and spearhead new projects, even if they are not well defined. </li>
<li>You build systems bigger than you. You contribute to open source, build packages your peers want to use, or design frameworks to elevate your team.  Reuse is a strategy, not a buzzword. </li>
</ul>
Skills needed to succeed:   
<ul>
<li>Bachelor's degree in Computer Science, Data Science, Biostatistics, Mathematics, or similar related field AND 5+ years of professional data science experience building predictive models </li>
<li>OR Master’s or Ph.D in Computer Science, Data Science, Biostatistics, Mathematics, or similar related field AND 3-5 years of professional data science experience building predictive models </li>
<li>Advanced proficiency with deep learning frameworks and the core Python data science ecosystem, and a deep understanding of modern AI architectures (e.g., Transformers, Neural Networks, LLMs) and related ML fields </li>
<li>Strong technical communication and collaboration skills, particularly communicating with nontechnical stakeholders and working collaboratively with engineering/product teams to meet project goals and timelines  </li>
<li>MLOps and hands-on experience taking models from notebook to production — Experiment tracking, packaging, containerization (Docker), model serving and APIs, CI/CD, and pipeline orchestration </li>
<li>Monitoring models in production – tracking data and concept drift and performance degradation, with alerting </li>
<li>Strong peer leadership and ability to mentor junior DS on best practices and problem solving.  Experience setting and improving team standards. </li>
</ul>
Bonus Qualifications:
<ul>
<li>Fluency with actuarial methods and working with actuaries is a plus  </li>
<li>Familiarity with healthcare and medical data </li>
<li>Familiarity with underwriting and claims, or predicting long-tailed and/or rare events </li>
</ul>
What We Offer:  
<ul>
<li>A fun, team-oriented startup culture.  </li>
<li>Generous stock options - we all get to own a piece of what we’re building.  </li>
<li>Unlimited vacation days.  </li>
<li>Flexible schedule that supports working from home.  </li>
<li>Full benefits package includes medical, dental, vision, 401k, paid paternal leave, and more.  </li>
<li>Ample opportunities to learn and take on new responsibilities.  </li>
</ul>
We are an equal opportunity employer. 
Salary Range: $160,000-195,000 base salary annually. 
This role is also eligible for an annual performance bonus, equity grant, and a comprehensive benefits package. In accordance with the Massachusetts Pay Transparency Law, we are providing a good-faith salary range for this position at the time of posting. The actual salary offered will depend on the level at which the candidate is hired, as well as their experience, skills, qualifications, and location. Compensation may grow over time through merit-based increases, promotions, and company-wide adjustments. If your salary expectations fall outside this range, we still encourage you to apply so we can have a conversation.

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