Top Senior Data Science Jobs

Leads growth marketing analytics and measurement strategy, manages and develops a team of approximately three data scientists, and drives advanced statistical and machine learning initiatives. Oversees cross-channel performance analysis, causal measurement, experimentation, automation, data pipelines, scalable dashboards, and recurring reporting. Influences VP and SVP stakeholders, translates ambiguous business problems into analytical programs, establishes technical standards, and communicates insights to technical and executive audiences.

Supports data science teams by cleaning and preparing structured and unstructured datasets, automating ETL workflows, and developing scalable data pipelines. Uses Python, SQL, big-data frameworks, cloud services, and dashboarding tools to support analytics, modeling, LLM/NLP workflows, and operational data products. Maintains documentation, assists with data governance and visualization, and contributes to mission-focused intelligence solutions in a highly sensitive environment.

Uses advanced analytics, business intelligence, and data science to deliver strategic insights and solutions for AML and sanctions clients in banking and capital markets. Responsibilities include analyzing complex datasets, developing data pipelines and architecture, creating scalable visualization solutions, collaborating with teams, managing stakeholders, building client relationships, and solving complex problems. The role also involves mentoring and managing others while maintaining professional and technical standards.

Lead development of interactive safety visualizations, dashboards, analytical applications, data marts, and automated reporting for autonomous-driving safety. Build trusted, performant data products with quality monitoring, frequent refreshes, metric definitions, lineage, and statistical interpretation. Partner with safety engineers, data engineers, program teams, and executives to translate complex safety information into clear decision-support tools and continuous improvement insights.

Leads cybersecurity data science initiatives, including exploratory analysis, statistical modeling, machine learning, deep learning, feature engineering, model governance, and secure production coding. Partners with cybersecurity, engineering, and business stakeholders to define use cases, deploy reliable models, validate AI-assisted recommendations, establish AI workflow guardrails, and address security, resiliency, auditability, and regulatory requirements.

Leads data science and machine learning initiatives addressing cybersecurity and technology risk. Responsibilities include analyzing security datasets, developing and evaluating statistical and deep learning models, engineering features, preparing production code, supporting model governance, and establishing controls for AI-assisted workflows. The role partners with cybersecurity, engineering, data, and business stakeholders while ensuring solutions are secure, auditable, interpretable, resilient, and aligned with regulatory expectations.

Leads data science and machine learning initiatives addressing cybersecurity and technology risk challenges. Responsibilities include exploratory data analysis, statistical and deep learning model development, feature engineering, secure production coding, model governance, AI workflow guardrails, and stakeholder communication. The role also supports security control effectiveness, production model monitoring, and responsible deployment of AI solutions using enterprise-authorized capabilities.

Develop end-to-end AI and machine learning solutions for Product Safety, partnering with business stakeholders to identify opportunities, train and validate models, prototype generative and agentic AI solutions, and deploy solutions with MLOps in cloud environments. The role also involves statistical analysis, reporting, trend modeling, data testing, and communicating insights and recommendations to leadership and customers.

Develop machine learning models supporting credit underwriting and business card strategies. Partner with data scientists, software engineers, product managers, and business stakeholders to design, train, validate, evaluate, backtest, and implement models. Analyze large numeric and textual datasets using Python, AWS, Spark, H2O, and related technologies, translating complex findings into business outcomes.

Build predictive failure models, real-time anomaly detection systems, diagnostic tools, and operational dashboards for spacecraft manufacturing, testing, and integration. Apply statistical modeling, causal inference, reliability analysis, and data mining to identify root causes, bottlenecks, quality issues, supplier risks, and process improvements. Develop reproducible Python and SQL analyses that support engineering decisions and mission reliability.

Build predictive models, anomaly detection systems, diagnostic tools, dashboards, and statistical quality-monitoring solutions for spacecraft manufacturing and testing. Analyze telemetry, production, supplier, and reliability data to identify failures, bottlenecks, delays, and root causes. Develop reproducible Python and SQL analyses and communicate actionable findings to engineering and operations stakeholders.

Lead people-analytics projects: analyze large HR datasets using statistical methods and ML (SAS/Python/R), build models and visualizations, deliver insights to stakeholders, and partner with data engineers and architects to operationalize solutions.

As a Senior Data Scientist, the role involves leveraging data analysis, experimentation, and statistical methods to influence business decisions and enhance Gusto's product performance, while mentoring team members.

Develop machine learning and statistical models across large numeric and textual datasets, from design through implementation. Collaborate with data scientists, software engineers, and product managers to deliver customer-focused products. Analyze data, validate and backtest models, apply techniques such as classification, clustering, sentiment analysis, time series, and deep learning, and translate technical findings into business goals.

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.

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.

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.

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.

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.

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 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.