Top Senior Data Science Jobs

Senior Data Scientist responsible for partnering with business leaders to frame ambiguous problems, deliver statistical and machine learning solutions, communicate insights, and drive measurable adoption. The role includes end-to-end analytical ownership, stakeholder influence, executive communication, Agile backlog management, cross-functional collaboration with engineering and technology teams, mentorship, governance, and improving analytics maturity.

Senior Data Scientist partnering with Wealth Management and Private Banking to drive acquisition, retention, and AUM growth. Responsibilities include developing and deploying predictive models, preparing and analyzing large datasets, measuring cross-channel marketing campaigns, standardizing KPIs, and translating findings into actionable recommendations. The role requires collaboration with business, marketing, technology, risk, and compliance stakeholders, along with clear communication of insights to nontechnical audiences.

Build and maintain Marketing Mix Models to measure marketing effectiveness, develop attribution metrics, and create optimization solutions for budget allocation and scenario planning. The role applies statistical, Bayesian, time series, regression, and optimization methods using Python and SQL. It partners with marketing and cross-functional teams to translate analytical findings into actionable business recommendations and influence investment decisions.

Serve as Netflix Animation Studios’ founding data scientist, developing measurement frameworks, forecasting and scenario-planning models, dashboards, and data products for slate planning, production efficiency, workforce allocation, and strategic decision-making. Partner with studio leadership, data scientists, analytics engineers, and data engineers to establish data foundations, define metrics, analyze operational drivers, and translate complex findings into actionable recommendations.

Develop data collection tools, visualizations, dashboards, algorithms, and scripts to analyze structured and unstructured data. Build predictive analytics and machine learning solutions, integrate models into production applications, and create API-based data pipelines. Translate complex data into actionable insights for Air Force mission leaders while managing enterprise-scale data science projects and communicating findings to technical and non-technical audiences.

Build core dbt and Snowflake data models, analyze consumer payments, credit, pricing, and risk behavior, design experiments, create dashboards, improve data quality, and set technical direction while mentoring analysts and engineers.

Own the data science strategy and experimentation roadmap for ranking and relevance. Partner with product, engineering, ML, and commercial teams to improve marketplace outcomes through ranking models, experimentation, observational analysis, KPI frameworks, and commercial impact measurement. Balance advertiser, user, and platform value; guide launch and investment decisions; communicate findings to stakeholders; mentor scientists; and develop reusable analytical methods.

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.

Develop fraud detection, risk assessment, anomaly detection, and risk-scoring models across factoring, payments, lending, and freight businesses. Analyze financial, behavioral, operational, and transaction data; identify fraud patterns; define risk signals and model metrics; communicate findings to stakeholders; and coordinate with Machine Learning teams to deploy models into production. Document methodologies and performance while partnering with Product, Credit, Risk, Operations, Data Services, and BI teams.

Develop and maintain predictive models for LTV, churn, retention, segmentation, personalization, and user acquisition decisions. Design experiments and causal analyses, apply LLMs to unstructured data, build AI-powered analytics tools, and productionize models with data engineering partners. Establish modeling standards, communicate insights to stakeholders, and mentor analysts and data scientists.

Serve as the analytics and data science partner for Workday Data Cloud, connecting demand, sales pipeline, product usage, and consumption-based pricing. Investigate ambiguous business questions, derive decision-ready insights, create visualizations and executive narratives, and advise senior stakeholders. Build curated data assets, improve data connections and pipelines, address instrumentation gaps, and collaborate with Product, Engineering, and GTM teams to support strategic growth and data-driven decision-making.

Leads data science for AML watchlist screening, developing multilingual NLP, entity resolution, information extraction, matching, risk-scoring, and graph-based detection systems. Builds data quality pipelines, evaluation frameworks, threshold tuning, backtesting, and production ML capabilities. Partners with Product and Engineering to deploy research at scale, leads technical initiatives, mentors peers, and translates model performance into compliance and business outcomes.

Develops and deploys AI, generative AI, LLM, and agentic workflow solutions for pharmaceutical therapeutics development and supply. Responsibilities include building retrieval and evidence synthesis systems, establishing LLM evaluation frameworks, applying responsible AI and governance practices, and maintaining software engineering standards. The role collaborates with CMC, regulatory, quality, scientific, manufacturing, supply chain, and AI/ML stakeholders to deliver scalable solutions in regulated environments.

Leads end-to-end research and development of statistical and machine learning models for media measurement. Analyzes large datasets, validates methodologies, collaborates with engineering and product teams on scalable model deployment, and communicates insights through visualizations and presentations. Requires advanced statistical expertise, machine learning proficiency, Python, and substantial independent data science experience.

Lead hands-on delivery of agentic conversational AI solutions for banking IVR systems using Amazon Connect and Amazon Lex. Conduct experimentation, interaction-data analysis, model evaluation, intent and slot tuning, prompt development, and NLU performance optimization. Support LLM and GenAI capabilities, identify risks, and communicate findings and recommendations to leadership and project stakeholders.

Develop machine learning, statistical, graph-based, and data-mining algorithms for national-security missions. Build, optimize, and validate AI/ML and large language models; create feature vectors, experiments, simulations, visualizations, and predictive analytics; and implement prototypes in production frameworks. Collaborate with subject-matter experts to automate analysis, guide analytic development, and deliver data-driven insights. Experience with LLMs, RAG, agents, GPU computing, and active TS/SCI clearance with polygraph is required.

Provide data analysis, graphics, and dashboards to identify challenges and opportunities. Use Python or R, SQL, relational and NoSQL databases, Tableau, and time-series analysis to transform unstructured data into actionable insights supporting customer decisions about production, resources, and personnel.

Design and deliver end-to-end data science solutions for complex client and business problems. Apply machine learning, statistical modeling, and AI to develop scalable analytical products, translate business needs into analytical frameworks, and guide deployment. Partner with clients and stakeholders, communicate insights, uphold technical and governance standards, mentor junior team members, and support business development through solution scoping, proposals, and presentations.

Develop proprietary foundation models, fine-tune domain-specific models, and architect multi-agent systems with autonomous reasoning, tool calling, and advanced RAG. Build curated and synthetic datasets for pretraining, alignment, and benchmarking. The role requires deep generative AI expertise, including LLM development, agent frameworks, foundation model architecture, and production-grade RAG pipelines, along with proficiency in modern model training, inference, vector infrastructure, and data technologies.

Lead data science projects across R&D, manufacturing, sales, and supply chain. Prepare and clean data, build regression and machine learning models (random forest, xgboost, lasso), perform cross-validation and hyperparameter tuning, query data with SQL, create plots and reports, communicate results to nontechnical stakeholders, identify high-value problems, and collaborate with data engineering and software teams to deploy models.

Leads data science initiatives by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Develops predictive models and algorithms, translates complex datasets into actionable insights, advises clients, and guides teammates. Requires Python, SQL, statistical programming, TS/SCI clearance, DoD 8140 Level II certification, and up to 25% travel. Intelligence and geospatial analytics experience are preferred.

Develops and deploys AI and machine learning models, algorithms, and analytics for federal customers. Responsibilities include analyzing datasets, testing and refining models, developing statistical and graph-based algorithms, creating visualizations, automating manual analysis, and integrating prototype solutions into production analyst workflows. Requires quantitative education, extensive data analysis and programming experience, and a TS/SCI clearance with polygraph.

Develop AI-enabled supply risk analytics using machine learning, probabilistic modeling, Monte Carlo simulation, forecasting, anomaly detection, and Generative AI. Build interactive applications, data pipelines, agentic workflows, and reporting automations using Python, SQL, Databricks, AWS Glue, and related tools. Collaborate with global Supply Chain, Quality, Procurement, and Business Continuity stakeholders to translate risk questions into actionable insights while following responsible AI, data privacy, quality, testing, and agile development practices.

Lead data science efforts for federal customers: analyze large structured and unstructured datasets, build predictive models and ML algorithms, automate analytic workflows, mentor teammates, brief military leaders, translate technical requirements for non-technical stakeholders, and support deployments and CONUS/international travel as needed.

Analyze complex operational, program, schedule, requirements, risk, and workstream data to create trusted metrics, dashboards, models, and decision-support products. Define KPIs and analytical logic, assess data quality, develop Python/R/SQL analyses, and support AI-enabled workflows. Partner with architects, engineers, leadership, and mission stakeholders to translate ambiguous operational questions into measurable insights and recommendations for FAA modernization and Digital Command Center initiatives.