Top Hybrid Data Science Jobs

Build and deploy enterprise-scale GenAI, RAG, multimodal, and multi-agent systems for growth marketing. Responsibilities include LLM training and adaptation, memory and retrieval architectures, behavioral embeddings, agentic interfaces, observability, safety, evaluation, and scalable production inference. The role requires end-to-end AI product development, collaboration with product and engineering teams, technical leadership, mentoring, and external representation through publications, patents, conferences, or open-source contributions.

Leads an applied science team developing scalable machine learning and data science solutions for pricing and monetization. Responsibilities include architecting models, providing technical guidance, setting strategy and roadmaps, managing execution, hiring and mentoring scientists, and collaborating with engineering, product, analytics, and operations teams. The role also contributes to causal inference, stochastic optimization, demand estimation, and experiment design research.

Develops data science and machine learning solutions for auto finance products. Analyzes digital customer behavior, builds predictive models, evaluates A/B experiments, and creates measurement frameworks and KPIs to improve conversion and engagement. Partners with Product, Technology, Design, Risk, and business leaders to translate complex questions into actionable insights, communicate recommendations, and support strategic decisions. May also build conversational AI solutions and integrate machine learning services into applications.

Lead customer insights and big data analytics initiatives for Chase Merchant Services. Build scalable datasets and pipelines with Python and SQL, develop quantitative analyses, and deliver commercially actionable recommendations. Design and evaluate prompts, LLM-powered assistants, retrieval strategies, and agent workflows. Monitor GenAI performance, improve solution quality, and collaborate with product managers, engineers, data owners, subject matter experts, and senior stakeholders.

Develop, transform, and model cybersecurity data for classified Intelligence Community customers. Extract and manipulate datasets, analyze network and operating-system logs, build workflows and predictive models, conduct statistical forecasting, and manage and troubleshoot data feeds. The role also involves using Splunk and data-transfer tools across complex shared networks and security enclaves, with opportunities to apply SecDevOps practices, cloud security controls, scripting, and hybrid cloud technologies.

Develop predictive models, business intelligence solutions, and descriptive, predictive, and prescriptive analytics for financial services clients. Lead client engagements from design through implementation, audit, and presentation. Build data extraction and statistical analysis programs using R, Python, SQL, Hive, and Spark. Support sales initiatives, identify opportunities for analytic products, deliver recommendations, and collaborate with cross-functional teams.

Lead measurement and data science strategy for Klaviyo’s AI agents, Composer and Customer Agent. Develop frameworks for adoption, quality, engagement, customer value, cost, and revenue impact. Apply experimentation, causal inference, statistical modeling, conversation analysis, human evaluation, and LLM-based evaluation. Partner with Product, Engineering, Finance, and executives, build governed metrics and scalable analytics, influence product and pricing decisions, and mentor data scientists.

Analyze large datasets and conduct statistical, causal, and experimental analyses to answer product and business questions. Build dashboards and visualizations, investigate metric changes, identify data quality issues, and communicate actionable insights. Collaborate with Product, Engineering, and Data Engineering to validate metrics, experiments, and measurement approaches, while documenting methodologies and assumptions.

Build, validate, deploy, and manage machine learning models that detect small-business fraud, including transaction fraud, account-opening fraud, and account takeover. Partner with data scientists, software engineers, and product managers to analyze large-scale numeric and textual datasets, translate complex findings into business outcomes, and develop data science solutions using Python, cloud platforms, and open-source technologies.

Develop and validate clinical heuristics and algorithms using real-world healthcare data. Build Python profiling pipelines, automated data-quality checks, and longitudinal patient cohorts. Analyze complex clinical databases and ETL pipelines, evaluate AI, NLP, and heuristic outputs, and collaborate with clinical, labeling, engineering, and data science teams. Maintain documentation, configuration, metrics, and validation standards for transparent, high-quality clinical analytics.

Develops claim-level reserve modeling and predictive analytics for personal and commercial insurance lines. Responsibilities include mining large datasets, applying statistical and machine learning techniques, testing hypotheses, building predictive models, creating accessible visualizations, and translating findings into business decisions. The role also advances analytical methodologies, guides complex projects, advises stakeholders, customizes solutions, and leads cross-functional data science initiatives.

Leads data science strategy for provider growth, including experimentation, marketing and lifetime-value measurement, forecasting, causal inference, predictive modeling, and product analytics. Manages and develops a data science team, creates roadmaps, partners cross-functionally, identifies provider growth opportunities, and translates complex analyses into executive recommendations. The role requires hands-on work with SQL, Python or R, data modeling, and dbt.