Top Data Science Jobs

Develops data integration workflows, dashboards, statistical and predictive models for biotherapeutics process development. Analyzes complex scientific and process datasets, identifies trends and anomalies, supports process improvement, and communicates findings to technical teams. The role also troubleshoots data and software issues, trains team members, collaborates externally, and contributes to project deliverables and modeling initiatives.

Develop machine learning and statistical models for Business Cards marketing, including design, training, evaluation, validation, and implementation. Analyze large numeric and textual datasets using Python, cloud computing, and big-data technologies. Partner with data scientists, engineers, product managers, analysts, and marketers to translate insights into personalized campaigns and measurable business outcomes.

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.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Develops data-driven insights for Business Banking deposit and cash management products. Partners with product managers to translate business questions into analyses, forecasting, diagnostic deep dives, and performance reporting. Communicates findings to nontechnical stakeholders, supports product strategy and customer engagement, and maintains strong data quality and controls. The role requires advanced SQL, experience across on-premises and cloud databases, and use of large language models to improve code development.

Develop predictive models, forecasting solutions, machine learning algorithms, statistical analyses, optimization and simulation models, and generative AI applications for business functions. Analyze large datasets, identify trends and opportunities, and communicate insights through visualizations, reports, and presentations. Collaborate with business partners to translate complex challenges into data-driven recommendations.

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.

Lead end-to-end revenue forecasting, developing production statistical and machine learning models, setting evaluation and governance standards, investigating performance drivers, and building scalable self-service analytics. Partner with Product, Finance, Engineering, and Go-to-Market leaders to support strategic decisions, mentor data scientists, guide technical direction, and communicate complex findings to executive and non-technical audiences.

Lead claims-based studies and actuarial modeling to quantify Nourish’s impact on healthcare spending, utilization, total cost of care, PMPM savings, and ROI. Apply causal inference, risk adjustment, and matched comparison methods to evaluate care programs. Translate findings into reports and executive narratives for health plan partners while building reusable datasets and analytical workflows with Analytics Engineering.

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.

Leads data science initiatives for financial services pricing by partnering with engineers and product managers, analyzing large-scale data, and developing machine learning models from design through implementation. Responsibilities include statistical modeling, validation, backtesting, translating technical findings into business outcomes, evaluating emerging technologies, and developing team talent.

Analyzes structured and unstructured datasets; develops algorithms, dashboards, queries, and statistical models; transforms and mines data; applies mathematics, statistics, operational mathematics, and machine learning; interprets data and prepares predictions; and collaborates with business units to drive solutions. The role requires 6–9 years of experience with a bachelor’s degree or 4–7 years with a master’s degree, plus CUI and OPSEC certification eligibility.

Develop and implement NLP, machine learning, and AI solutions for People Strategy and Analytics. Build and evaluate LLM applications using prompt engineering, retrieval-augmented generation, and open-source tools. Collaborate with data scientists, engineers, analysts, product managers, and HR stakeholders to create AI-powered talent tools, analyze employee behavior, and inform workforce strategy.

Lead data science for lead valuation, predictive close scoring, revenue optimization, and constrained allocation across a national marketplace. Build inventory valuation, pricing, forecasting, experimentation, simulation, and optimization models using methods such as mixed-integer and stochastic programming. Partner with Product, Pricing, Marketing, Finance, and Engineering to deploy models into production decisioning, monitor KPIs, and communicate tradeoffs to executive stakeholders.

Apply statistical analysis, machine learning, time-series methods, and AI agent technologies to semiconductor manufacturing data. Extract and prepare data with Python and SQL, identify trends and anomalies, evaluate LLM workflows, and build reproducible analytics, visualizations, dashboards, and reports. Present technical findings and recommendations to engineering teams while supporting manufacturing improvement initiatives.

Build and own Legora’s Growth data foundation, including trusted dbt models, data pipelines, shared GTM metrics, dashboards, and data activation into business systems. Partner with Marketing, Sales, RevOps, and Customer Success to analyze funnels, attribution, pipeline, retention, expansion, and customer behavior. Improve data quality, conduct business deep dives, and establish scalable data-team standards and tooling.

Analyze acquisition data and guide marketing strategy, budget allocation, channel optimization, and growth decisions. Design incrementality experiments, develop marketing mix and allocation models, define acquisition metrics, identify new growth opportunities, and improve web conversion. Partner with Marketing, Engineering, Finance, Design, and User Research to translate statistical and machine learning insights into measurable business outcomes across paid, organic, consumer, and enterprise channels.

Develops end-to-end data science solutions, including ETL, statistical and machine learning model development, validation, deployment, and impact measurement. Collaborates with cross-functional teams to implement production models, improve data quality, identify correlations, and communicate insights to business stakeholders. Requires Python, SQL, analytical modeling, and familiarity with Spark and Hive.

Leads data science for Capital One’s US Card business, developing and deploying machine learning models across marketing, acquisitions, underwriting, and fraud prevention. Partners with data scientists, engineers, and product managers; analyzes large-scale numeric and textual data using cloud and open-source technologies; translates complex insights into business outcomes; and develops talent while guiding technical innovation.

Lead data science for Capital One’s US Card organization by building and deploying machine learning models across the credit card lifecycle. Partner with software engineers and product managers, analyze large-scale customer data using Python, cloud, and distributed computing technologies, and translate statistical insights into business outcomes. The role also emphasizes innovation, stakeholder communication, technical leadership, talent development, and managing complex data science initiatives.

The Data Scientist will use SQL, Python, statistics, machine learning, and AI/LLMs to solve business problems involving customer behavior, marketing effectiveness, conversion, revenue, and forecasting. Responsibilities include data preparation, feature engineering, predictive modeling, experimentation, workflow automation, model deployment and monitoring, and communicating actionable recommendations to technical and business stakeholders.

Develops statistical and machine-learning models for pricing, revenue optimization, demand forecasting, segmentation, and experimentation. Prepares large datasets using Python or R and SQL, validates and documents models, and partners with data engineering teams on scalable pipelines and production deployment. Translates analytical findings into pricing recommendations, dashboards, and executive insights while supporting governance, monitoring, and analyst enablement.

Leads advanced data science, machine learning, AI, predictive analytics, anomaly detection, and NLP solutions for Internal Audit. Develops production-ready models and analytical frameworks, supports risk identification and continuous monitoring, and partners with audit, technology, and data teams. Communicates insights to technical and business stakeholders, ensures governance and validation standards, and mentors team members. The role requires strong Python and SQL skills, large-scale data experience, and expertise in deploying and monitoring analytics solutions.

Develop and refine credit risk, pricing, and predictive models for consumer lending. Design A/B testing frameworks, analyze model performance, collaborate with strategy and operations teams, and present data-driven recommendations to senior management. The role requires statistical analysis, machine learning, Python, SQL, independent problem-solving, and effective communication across teams.