Top Data Science Jobs

The Data Scientist will analyze complex datasets, build predictive models, collaborate with stakeholders, and present insights for business decisions.

Develop and deploy applied generative AI and machine learning solutions using text, image, and audio data. Build, train, evaluate, validate, and operationalize NLP and deep learning models with LLMs, VLMs, and cloud technologies. Define AI observability and audit requirements, monitor non-deterministic system behavior, establish controls, curate training datasets, and collaborate with product, engineering, business, and data science teams to deliver scalable customer-facing solutions.

Performs exploratory data analysis, feature engineering, predictive modeling, statistical testing, and data visualization on large structured and unstructured datasets. Develops, validates, and improves predictive and prescriptive models, identifies business opportunities, and presents findings to stakeholders. Collaborates with machine learning engineers, product, and engineering teams on model deployment and internal data platforms. Establishes modeling best practices, supports data collection, and mentors associate data scientists.

Leads data science for credit card acquisition decisioning, including systemic risk monitoring, statistical analysis, vulnerability detection, and multi-model infrastructure. Builds LLM and Agentic AI solutions, develops machine learning models, analyzes large-scale data, and partners with data scientists, analysts, and software engineers. Translates complex technical findings into business goals while guiding innovation and talent development.

Performs exploratory data analysis, feature engineering, predictive and prescriptive modeling, statistical testing, visualization, and business problem solving. Develops, validates, and deploys models in partnership with engineering and product teams, improves data sources and internal data platforms, communicates findings to stakeholders, and mentors associate data scientists.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Build and validate predictive lending models, analyze consumer risk and portfolio impact, implement and monitor models with engineering, develop tooling and automated SQL/Python dashboards, design A/B tests, and communicate actionable insights to senior management.

Apply statistical inference and experimentation to evaluate product changes, identify Growth opportunities, and guide strategy. Partner with product, engineering, and marketing leaders to define metrics, build datasets and dashboards, and communicate insights that drive business and product decisions. The role requires SQL, scripting, data transformation, and practical tradeoffs between speed and accuracy. Predictive modeling experience is preferred, as are startup and B2B SaaS backgrounds.

Develop predictive pricing and underwriting models for property and specialty insurance products. Responsibilities include manipulating large datasets, testing hypotheses, evaluating statistical significance, creating visualizations, supporting pricing program deployments, adjusting models for state regulations, preparing regulatory documentation, and responding to Department of Insurance objections. The role collaborates with business stakeholders and data science teams to deliver data-driven solutions and explain technical findings to nontechnical audiences.

Build and deploy data science models for CPG brands, focusing on demand and sales forecasting, promotion effectiveness, pricing, elasticity, and cannibalization. Analyze messy retail, inventory, trade promotion, and supply-chain data; develop statistical validation and evaluation methods; and communicate model insights and uncertainty to technical and non-technical stakeholders. Collaborate with AI/ML engineers to move analyses into production.

Own the analytics roadmap for a Consumer Banking customer pillar, leading measurement, experimentation, and analyses across growth, retention, pricing, servicing, and strategy. Translate complex business questions into executive-ready recommendations, influence cross-functional decisions, establish analytical standards, and mentor analytics talent. The role requires strong statistical, experimental, communication, visualization, and stakeholder-management skills.

The Director of Data Science role at Capital One involves leading a team to build data pipelines and machine learning models, leveraging technologies like Python and AWS to derive insights for improving customer financial decisions.

Build predictive models for churn, expansion, and look-alikes; run marketing mix and incrementality analyses; create leading indicators and cohort reporting; partner with Sales, Marketing, and RevOps to translate analytics into actionable recommendations and inform data infrastructure needs.

Build and maintain pricing and profitability models to optimize lending strategies, incorporating proprietary credit models. Design A/B tests, analyze financial performance trends, and present data-driven recommendations to senior management. Collaborate with credit risk, capital markets, FP&A, marketing, and sales to quantify financial impact and improve pricing decisions.

Lead commercial data science for Headway's commercial business: own roadmap, run proactive strategic analyses for executive accounts, monitor key metrics, enable self-service analytics, mentor data scientists, and translate technical findings into actionable recommendations tied to measurable outcomes.

Leads product analytics for small business lending by framing business questions, building and validating datasets, developing KPI and reporting frameworks, conducting statistical and predictive analyses, and translating findings into executive-ready recommendations. Partners with product, lending strategy, risk, finance, technology, and operations stakeholders to improve measurement, automation, data governance, customer understanding, and lending-product performance.

Develops end-to-end data science and generative AI solutions for payments and merchant services. Responsibilities include designing prompts, building chat assistants and workflow automations, creating Python and SQL data pipelines and Tableau dashboards, testing and monitoring AI models, developing algorithms and data models, and delivering customer insights and analytics. The role collaborates with business stakeholders and data teams to improve decision-making, automate processes, and support commercial growth.

As a Staff Data Scientist at Gusto, you will drive data-informed decision-making, lead projects, collaborate with teams, and mentor others in statistical analysis and experimentation.

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

The role involves analyzing fraud and risk data, defining KPIs, collaborating on anti-fraud strategies, and creating tools and dashboards for data accessibility at Whatnot.

Develop and productionize AI and machine learning solutions for client, investment, and operational problems. Responsibilities include designing AI agents and agentic workflows, prototyping with foundation models and agent frameworks, building evaluation and observability systems, ensuring reliability and compliance, translating business needs into technical solutions, leading major initiatives, and presenting research and technical work to senior stakeholders.

Perform advanced analytics for Home Lending Sales strategy, integrating data and delivering descriptive, diagnostic, and predictive insights. Build and maintain Tableau dashboards, automate reporting, identify business performance drivers, and recommend KPI improvements. Collaborate with Sales, Marketing, Product, Finance, and technical partners to develop data-driven solutions and apply AI and machine learning to automate innovative business processes.

Lead product-focused data science for Cash App Lending: define measurement frameworks, run experiments, analyze customer and risk data, use AI tools to accelerate analysis, partner with product/engineering/risk, drive technical standards, and mentor junior data scientists to inform product and policy decisions.