Gen Digital

We are seeking a Data Scientist to strengthen our ability to identify which customer behaviors and treatments truly drive business outcomes and to turn those findings into better decisions across the customer lifecycle. This role will sit at the intersection of analytics, experimentation, and business strategy, with a primary focus on causal inference, treatment evaluation, and the translation of data into actionable recommendations. The need is grounded in the current CSM mission to generate behavioral intelligence, causal insights, and decision policies, as well as an active roadmap that already includes causal analysis, treatment evaluation, lead indicators, semantic-model development, and related data/measurement priorities across Norton and Freemium.Why this role is neededThis is not a generic analytics backfill. The role is needed to address a specific capability gap in causal measurement and experimentation support that cannot be fully absorbed by existing analytics capacity or by tooling alone. Current work is already distributed across analytics and reporting, treatments, portal, and data/AI ownership, while emerging agent-based tooling improves access and speed but still depends on strong statistical judgment, sound methodology, and clear experiment design to produce trusted outcomes.Core responsibilitiesDesign and execute causal analyses to determine whether changes in behavior, treatments, campaigns, and product experiences have a measurable impact on key KPI’sPartner with analytics, treatments, product, and data stakeholders to shape experiment design, measurement plans, and success criteria before launchesSupport A/B test analysis, quasi-experimental analysis, and post-readouts with clear business interpretation and recommendationsIdentify key drivers of KPI movement and translate analytical findings into specific actions for lifecycle strategy, targeting, and treatment designHelp define robust measurement frameworks for lead indicators, customer journeys, and treatment performanceImprove confidence in analysis by ensuring appropriate validation logic, data definitions, and measurement consistency are in place for causal workContribute to the evolution of data products, semantic models, and analytical workflows that make experimentation and causal readouts more scalableCommunicate findings clearly to business leaders and cross-functional partners, focusing on implications, tradeoffs, and next-step decisionsRequired capabilitiesStrong grounding in statistics, experimental design, and causal inferenceExperience analyzing A/B tests or other treatment/intervention programs in a business settingAbility to work with complex behavioral, customer, or lifecycle data and turn ambiguous questions into clear analytical plansStrong SQL skills and practical experience using Python or similar tools for analysisAbility to evaluate data quality, identify risks to valid inference, and apply appropriate checks before drawing conclusionsComfort working across technical and non-technical teams and translating technical findings into business languageGood judgment on when to use descriptive analytics, predictive methods, or causal approaches depending on the decision contextPreferred experienceExperience in subscription, lifecycle, CRM, growth, retention, or customer strategy analyticsExperience with BigQuery, modern cloud analytics environments, or large-scale customer datasetsFamiliarity with contribution analysis, driver analysis, uplift thinking, or heterogeneous treatment effectsExposure to building or supporting reusable analytics assets such as measurement frameworks, standardized readouts, or internal data science toolingSuccess measuresSuccess in this role would include:Improved quality and speed of causal readouts for key treatments and business initiativesBetter clarity on which interventions materially move retention, expansion, engagement, and related KPI’sStronger experimental design and measurement discipline upstream of launchesMore trusted and repeatable analytical outputs that business stakeholders can use to make decisions with confidenceClearer translation of analytical findings into actionable recommendations for roadmap and treatment prioritizationRole profileLevel: Data ScientistOrientation: Individual contributorPrimary emphasis: Causal inference and experimentationKey interfaces: Analytics and Reporting, Treatments, Product/Portal partners, Data and AI, and business stakeholders across the customer lifecycle

Data / ML / Analytics

About the RoleThe Kuala Lumpur office is the technology powerhouse of MoneyLion, where we measure anything and everything and rely on data to guide our decisions: both long-term strategies and day-to-day operations.Combine that backdrop with an incredible volume and sophistication of data and mix in MoneyLion’s voracious appetite for adopting paradigm-shifting technologies and you have the mission and mandate of the Data Engineering team. Now turbo-charged with a planetscale mandate as part of Gen Digital, we aim to leverage tried-and-true methodologies with game-changing technology to create a AI-native, scalable, extensible, and cost-effective data infrastructure across the Gen Digital enterprise and beyond.As a Data Engineer, you are a technical Individual Contributor (IC) figure within the team that will design, implement and optimize a Snowflake-centric architecture that will leverage many components of the modern data stack. You will be a trusted subject matter expert on dimensional data modeling, performant SQL query writing, infrastructure optimization, process automation and governance. You will be given the opportunity to leverage the latest and greatest AI tools and features, including Claude Code (Enterprise) and Snowflake’s Cortex Code, among many others.You will be responsible for designing and implementing an analytical environment using in-house and third-party tools, using Python and/or Java to automate data activities and enable efficient processing of data that is growing in both volume and complexity.You will design and implement complex data pipelines and data models for analytical consumption.You will write scalable and performant SQL queries running over billions of rows of data and design workflows aimed to ruthlessly streamline the governance of our complex layers of storage. You should have deep experience in designing and managing large datasets and pipelines to enable business use-cases. You should be an authority at designing, implementing and operating solutions that are scalable, stable and cost-efficient. Above all, you will be an authority on crafting thoughtful solutions that will empower absolutely everyone at Gen Digital towards an agentic AI-first analytics and development lifecycle.Key Responsibilities<ul><li>Extensive expertise operating &amp; optimizing a modern data warehousing solution (Snowflake preferred) from both performance and cost perspectives.</li></ul><ul><li>Expertise with Apache Kafka, real-time data processing, and event-driven architectures</li><li>Experience with Apache Iceberg, data lakehouse architectures, and modern table formats</li></ul><ul><li>Collaborate with data scientists, business analysts, product managers, software engineers and other data engineers to develop, implement and validate deployed data solutions.</li></ul><ul><li>Build and maintain data pipelines from internal databases and SaaS applications</li></ul><ul><li>Implement better solutions for data governance at scale, encompassing lineage, discoverability, and regulatory compliance.</li></ul><ul><li>Advocate best practices, uphold the highest standards for code maintainability and performance, and constantly champion our team’s core values of psychological safety and building in public.</li></ul><ul><li>Lead the team in honing our ways of working, coaching and mentoring junior engineers, and managing senior stakeholders from external departments.</li></ul>About You<ul><li>Bachelor's degree in computer science, Data Engineering, or related technical field</li></ul><ul><li>1-4 years of hands-on data engineering experience in large-scale, high-volume environments</li></ul><ul><li>Expert-level proficiency in SQL optimization for multi-petabyte datasets and advanced Python programming</li></ul><ul><li>Experience with Snowflake architecture, optimization, and cost management at enterprise scale</li></ul><ul><li>Demonstrated expertise building Kimball dimensional data warehouses and star schema architectures, or equivalent experience with thoughtful, non-generic projects</li></ul><ul><li>Advanced experience with AWS cloud infrastructure and either DBT or SQLMesh for data transformations</li></ul><ul><li>Familiarity with AWS is a big plus</li></ul><ul><li>Experience in planning day to day tasks, knowing how and what to prioritise and overseeing their execution </li></ul>What's Next...After you submit your application, you can expect the following steps in the recruitment process:<ol><li>Online Preliminary Codility test</li><li>Recruiter Screening Call</li><li>Take-home Assessment </li><li>Take Home Discussion (Virtual)</li><li>Interview - Hiring Manager (Virtual or face-to-face), 1.5 hours</li></ol>

Data Engineer - MoneyLion

About the RoleThe Kuala Lumpur office is the technology powerhouse of MoneyLion. We pride ourselves on innovative initiatives and thrive in a fast paced and challenging environment. Join our multicultural team of visionaries and industry rebels in disrupting the traditional finance industry!At MoneyLion, we measure everything and rely on data to guide our decisions, including both long-term strategies and day-to-day operations. As a Senior Data Engineer, your main goal is to support data scientists, analysts and software engineers by providing maintainable infrastructure and tooling they can use to deliver end-to-end solutions to business problems. You will work with terabytes to petabyte-scale data, in a complex data environment supporting multiple products and data stakeholders across the US and KL.You will be responsible for designing and implementing an analytical environment using in-house and third-party tools, using Python and/or Java to automate data activities and enable efficient processing of data that is growing in both volume and complexity. You will design and implement complex data pipelines and data models for analytical consumption. You will work with Redshift, Snowflake, EMR, Kubernetes, Airflow and more as the main tools of the job. You will write scalable and performant SQL queries running over billions of rows of data, and help simplify these processing to enable insights to be more easily extractable from them. You should have deep experience in designing and managing large datasets and pipelines to enable business use-cases. You should be an authority at designing, implementing and operating solutions that are scalable, stable and cost-efficient. Key Responsibilities<ul><li>Design, implement, operate and improve the analytics platform</li><li>Design data solutions using various big data technologies and low latency architectures</li><li>Collaborate with data scientists, business analysts, product managers, software engineers and other data engineers to develop, implement and validate deployed data solutions. </li><li>Maintain the data warehouse with timely and quality data</li><li>Build and maintain data pipelines from internal databases and SaaS applications</li><li>Understand and implement data engineering best practices</li><li>Improve, manage, and teach standards for code maintainability and performance in code submitted and reviewed </li><li>Mentor and provide guidance to junior engineers on the job</li></ul>About You<ul><li>Expert at writing and optimising SQL queries</li><li>Proficiency in Python, Java or similar languages</li><li>Familiarity with data warehousing concepts</li><li>Experience in Airflow or other workflow orchestrators</li><li>Familiarity with basic principles of distributed computing</li><li>Experience with big data technologies like Spark, Delta Lake or others</li><li>Proven ability to innovate and leading delivery of a complex solution</li><li>Excellent verbal and written communication - proven ability to communicate with technical teams and summarise complex analyses in business terms</li><li>Ability to work with shifting deadlines in a fast-paced environment</li></ul>Bonus Points<ul><li>Authoritative in ETL optimisation, designing, coding, and tuning big data processes using Spark</li><li>Knowledge of big data architecture concepts like Lambda or Kappa</li><li>Experience with streaming workflows to process datasets at low latencies</li><li>Experience in managing data - ensuring data quality, tracking lineages, improving data discovery and consumption</li><li>Sound knowledge of distributed systems - able to optimise partitioning, distribution and MPP of high-level data structures </li><li>Experience in working with large databases, efficiently moving billions of rows, and complex data modelling</li><li>Familiarity with AWS is a big plus</li><li>Experience in planning day to day tasks, knowing how and what to prioritise and overseeing their execution</li></ul>What's Next...After you submit your application, you can expect the following steps in the recruitment process:<ol><li>Online Preliminary Codility test</li><li>Recruiter Screening Call</li><li>Take-home Assessment </li><li>Take Home Discussion (Virtual)</li><li>Interview - Hiring Manager (Virtual or face-to-face), 1.5 hours</li></ol>

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Data / ML / Analytics

Data Engineer - MoneyLion

Senior Data Engineer - MoneyLion

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