Top Senior Data Science Jobs

Leads experimentation, causal inference, advanced statistical analysis, machine learning, and scalable data-product development. Partners with product, engineering, and research teams to generate insights, identify opportunities, influence executive decisions, and shape product strategy. Owns cross-functional projects, analytics roadmaps, reporting, stakeholder adoption, and methodological standards while translating complex data into actionable business outcomes.

Owns experimentation, business analytics, reporting, and advanced analysis for Realtor.com’s RDC Next platform. The role analyzes consumer and agent behavior, designs A/B and quasi-experiments, builds trusted datasets and dashboards, and delivers recommendations that influence product and business strategy. It also develops AI-enabled analytics workflows using LLMs, RAG, and agentic tools; partners with Product, Engineering, Finance, Marketing, and data teams; mentors analysts; and helps establish analytical standards and scalable data solutions.

Leads Responsible AI data science initiatives by developing and validating AI testing protocols, automating analyses with Python, identifying PII and PHI vulnerabilities, and designing red-team scenarios for conversational AI. The role documents responsible AI practices, supports governance and regulatory compliance, communicates findings to legal, compliance, and technical stakeholders, and mentors colleagues. It requires expertise across traditional machine learning, generative AI, agentic techniques, AI ethics, fairness, and the full AI lifecycle.

Leads product analytics, experimentation, and advanced analyses for consumer content and visualization experiences. Defines success metrics, designs tests, improves tracking and data quality, builds ETL pipelines and AI-enabled self-service reporting, and translates consumer behavior insights into product and business strategy. Partners with Product, Engineering, Finance, and data teams to identify opportunities, measure performance, and influence the product roadmap.

Senior analytics partner leading product experimentation, business analytics, consumer behavior analysis, and performance measurement for content and visualization experiences. Responsibilities include exploratory analysis, multivariate testing, metric definition, data quality improvement, ETL pipeline development, self-service AI-enabled reporting, and translating statistical or predictive insights into product and business strategy. The role collaborates with Product, Engineering, Finance, and data teams to influence roadmap decisions and improve consumer engagement.

Leads CRM marketing analytics for Seller and Owner verticals by developing segmentation, targeting, personalization, customer journey, and campaign measurement strategies. Analyzes consumer behavior to improve engagement, retention, lead generation, cross-selling, and revenue. Builds statistical and machine learning models, integrates CRM data, establishes KPIs, communicates insights to stakeholders, and partners with Marketing, Data Engineering, and IT teams.

Develop CRM marketing strategies for Seller and Owner audiences using segmentation, personalization, customer journey analysis, statistical modeling, and machine learning. Measure campaign effectiveness through KPIs and analytical frameworks, identify engagement and retention opportunities, and communicate actionable insights to technical and non-technical stakeholders. Collaborate with Marketing, Data Engineering, and IT teams on data quality, infrastructure, and tailored analytical solutions.

Lead the design and production deployment of large-scale operations research and optimization solutions for pricing, inventory, supply chain, and marketing decisions. Build LP, MILP, and MINLP models; use optimization solvers; develop APIs, data pipelines, monitoring, and automation; evaluate solutions through scenario and sensitivity analysis; and communicate findings to stakeholders and leadership.

Develop machine learning models, statistical analyses, and automated data science processes to improve player acquisition, retention, monetization, and experience. Extract insights from structured and unstructured data, deploy and monitor production models, communicate findings to stakeholders, collaborate across data and business teams, mentor colleagues, and improve team processes.

Build production data science models from high-frequency aircraft telemetry, spatial data, and noisy ATC audio. Develop signals, features, weak-supervision labeling, evaluation methods, validation, and drift detection for unlabeled streaming data. Partner with engineers to deploy and operate models in production, while collaborating with aviation experts to translate operational knowledge into measurable intelligence.

Leads advanced data science modeling and fraud prevention strategies for consumer credit products. Analyzes complex datasets, designs and monitors fraud risk controls, balances customer experience with losses and operational costs, and communicates recommendations to technical and business stakeholders. The role also ensures data quality, drives analytical best practices, collaborates across risk, product, legal, compliance, and operations teams, and mentors junior data scientists.

Develop data-driven collections strategies and advanced models for Venmo BNPL and other PayPal products. Use SQL, Python, AI, experimentation, and analytics to create scalable portfolio measurement, A/B testing, and performance solutions. Partner with Credit Strategy, Fraud, Risk, Operations, Product, Technology, and Compliance teams; translate insights into business strategies; present recommendations to senior leaders; maintain data quality; and mentor junior data scientists.

Lead analytics for client engagements across data onboarding, study planning, predictive modeling, production deployment, and ongoing optimization. Develop models for audience targeting, segmentation, propensity scoring, and campaign optimization, primarily for commercial pharmaceutical applications. Partner with business, analytics, and IT stakeholders, lead technical workshops, present findings to senior audiences, and manage client relationships. Build machine learning pipelines using Python, SQL, and cloud technologies, while contributing client insights to internal product development.

Develop and deploy NLP, entity-extraction, and generative AI models for cyber datasets in a classified environment. Responsibilities include parsing logs, feature engineering, model evaluation, data-quality validation, Python workflow development, requirements translation, stakeholder demos, and collaboration with cyber experts and data engineers. The role is full-time and on-site at a secure government facility.

Develop revenue optimization and risk mitigation strategies using experimentation, causal inference, forecasting, Monte Carlo simulations, and machine learning. Build data pipelines, improve analytics infrastructure, generate insights for client-facing teams, and influence Product and Engineering roadmaps. The role requires strong strategic thinking, business impact orientation, and collaboration in a remote-first environment.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

Lead complex data analysis and model development projects, building and deploying predictive models, machine learning algorithms, and AI solutions. Partner with engineering, product, finance, business, and analytics teams to translate objectives into actionable insights. Validate, monitor, and refine model performance while communicating technical findings clearly. Guide junior data scientists, improve team practices, and contribute to the data science roadmap.

Leads advanced data science modeling and provides independent second-line oversight of Buy Now Pay Later credit risk, underwriting, account management, risk controls, and exposure limits. The role analyzes portfolio performance, ensures data quality, challenges credit strategies, communicates insights to technical and non-technical stakeholders, and mentors junior data scientists while collaborating across geographically distributed teams.

Analyzes pharmacy claims, dispensing, adjudication, and related healthcare data to develop clinical measures, dashboards, KPIs, medication cohorts, and actionable insights. Evaluates medication access, affordability, adherence, utilization, formulary performance, safety, and outcomes. Translates stakeholder questions into analytic specifications, validates measures, partners on evidence development, and supports governed reporting products while maintaining privacy and data governance standards.

Build and deploy applied data science models for auto insurance carrier matching, media buying, bidding, and ad serving. Own the full modeling lifecycle, including data discovery, validation, deployment, monitoring, and adaptation to late labels, sparse sessions, feedback loops, and market drift. Partner with business stakeholders and an ML engineer to scale production models and deliver measurable acquisition and ROAS outcomes.

Build and deploy AI and machine learning solutions for clinical development, evidence generation, trial design, and decision support. Apply statistical modeling, simulation, and experimental design to clinical, operational, and real-world data. Develop reproducible model pipelines, integrate algorithms into production applications, and support compliant, interpretable ML architectures. Collaborate with product, engineering, UX, and scientific teams while documenting modeling decisions and contributing to knowledge sharing.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Lead end-to-end yield optimization for Azure infrastructure by analyzing revenue, hardware, and capacity data. Develop statistical and machine-learning models for pricing, resource allocation, and demand elasticity; design experiments and causal inference analyses; build metrics and dashboards; and communicate actionable recommendations to technical, business, finance, and executive stakeholders. Mentor peers and guide cross-functional decisions involving cloud economics, pricing, customer behavior, utilization, and cost efficiency.

Leads people analytics research using causal inference, quasi-experimental methods, statistical modeling, and large workforce datasets. Partners with Recruiting, Compensation, Talent, Legal, and business leaders on hiring, retention, promotion, pay equity, quality-of-hire, and organizational effectiveness. Translates research into visualizations and actionable decisions, leads compensation analytics and governance reporting, and uses AI tools to accelerate analysis and synthesis.