Top Senior Data Science Jobs

Designs and maintains large-scale APIs and systems supporting money movement, translates product requirements into technical specifications, and guides product strategy. The role requires data science or quantitative modeling expertise, production model development, experimentation, SQL, Python or R, and translating analysis into business outcomes. Responsibilities also include reviewing API launches, ensuring system compatibility and extensibility, representing the team in technical discussions, mentoring engineers, and establishing engineering principles.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

Leads supply chain data science initiatives involving time series forecasting, demand planning, buying, inventory optimization, replenishment, and machine learning. Develops and productionizes advanced forecasting and optimization models, manages ML pipelines and deployment, documents methodologies, troubleshoots code, and collaborates with cross-functional stakeholders. Mentors data scientists, supports Agile processes, and provides technical leadership across model development, monitoring, retraining, and continuous improvement.

Own the scientific and product roadmap for monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead cross-functional delivery with Product, Engineering, Sales, and GTM; define measurement standards, evaluate trade-offs, support customer pilots, influence commercial decisions, and turn bespoke studies into scalable products. Remain hands-on in SQL, Python, statistical modeling, and causal inference while mentoring scientists and establishing reusable methods.

Own audience or measurement products for Fetch’s monetization business, translating advertiser goals and purchase behavior into predictive models, datasets, and causal studies. Build and validate audience expansion, measure advertising outcomes, assess bias and uncertainty, and improve products using campaign results. Partner with Product, Engineering, Sales, and GTM, present findings to customers, and develop reproducible analyses and production-ready assets.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

Build, evaluate, deploy, and monitor identity-resolution and record-matching models using large, noisy datasets. Design labeling and measurement strategies where no definitive answer key exists, use AI-assisted labeling, manage model retraining and lineage, review data quality, and communicate results and tradeoffs to product, engineering, and sales stakeholders. Mentor teammates and ensure responsible handling of personal data, opt-outs, retention, and lineage requirements.

Lead data science initiatives involving entity and identity resolution, knowledge graphs, information extraction, search, predictive modeling, and AI-enabled discovery. Design data architectures and machine learning workflows, establish model development and monitoring standards, guide scalable data products, evaluate emerging technologies, and brief senior leadership on AI strategy, technical risks, and investment opportunities.

Develop and deploy machine learning and predictive models for banking and financial services clients. Responsibilities include feature engineering, model training and validation, credit and risk modeling, fraud detection, production-ready Python development, stakeholder engagement, MLOps collaboration, model monitoring, and communicating technical findings and business impact.

Lead data science at Bonfire by solving complex analytical problems, building predictive models and scalable Databricks data pipelines, and shaping AI-powered products. Own customer projects from discovery through delivery, translate findings into clear reports and presentations, establish technical standards, identify new analytical products, and mentor future team members. The role involves working with large healthcare claims datasets and collaborating closely with customers and founders.

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.

Leads enterprise-scale data science and statistical modeling projects supporting government evaluations, ROI analysis, risk assessment, and intervention effectiveness. Develops, validates, automates, and delivers predictive models, dashboards, scorecards, and KPIs. Reviews analytical designs and code, establishes reproducible standards, advises government stakeholders, and mentors data scientists. Requires advanced Python or R and SQL skills, quantitative education, statistical modeling experience, and Top Secret clearance or eligibility for Top Secret/SCI.

Develop statistical models, forecasting methods, and machine learning algorithms for airline optimization and travel retailing. Source and manipulate data using multiple programming languages, deploy models in cloud environments, and collaborate with software engineers on production analytics tools. Define KPIs, monitor data and model performance, conduct proof-of-concept studies, and communicate insights to clients and internal stakeholders.

Develop scalable data science solutions for financial crime and fraud analytics, including classification, anomaly detection, entity resolution, and graph applications. Collaborate across teams to implement model pipelines, meet governance standards, and deliver production-ready machine learning solutions. Engage stakeholders, communicate complex findings clearly, mentor junior data scientists, and help ING advance its data-driven banking strategy.

Leads and develops AI, machine learning, and advanced analytics solutions that deliver customer value. Translates requirements into deployable solutions, identifies and prioritizes projects, collaborates with functional and IT leaders, mentors junior team members, and supports model validation, reproducibility, deployment, and continuous improvement initiatives.

Develops and evaluates algorithms for synthetic aperture radar imagery, conducts quality trade studies, prototypes analytical tools, generates confidence metrics, and refines verification and validation processes. Builds software and scripts in Python using Databricks and Jupyter Notebook, supports CI/CD workflows with GitHub, implements and trains algorithms, and delivers technical presentations and reports to government customers.

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.

The Senior Data Scientist will develop, validate, and operationalize AI models for diagnosing complex diseases, enhance data analysis processes, and establish MLOps infrastructure while leading data-driven initiatives.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Own end-to-end data science and analytics projects using Python, machine learning, and time-series analysis to improve drilling safety and efficiency. Develop emissions, advisory, monitoring, alerting, and equipment-failure prediction models. Mentor data scientists and analysts, communicate findings to technical and nontechnical stakeholders, maintain code standards, support analytics applications, and occasionally visit drilling rigs to understand operations and test technology.

Develops reliability, statistical, and data science models for solid rocket motor design, production, testing, and sustainment. Responsibilities include failure analysis, uncertainty quantification, manufacturing variability, predictive cost modeling, performance-test analysis, material-property databases, aging and service-life estimates, and technology roadmaps. The role collaborates extensively with engineering, quality, operations, supply chain, and program management teams.

Lead end-to-end data science and machine learning projects, from scoping ambiguous client problems through production deployment. Build and evaluate statistical and ML models, analyze large datasets, run experiments, collaborate with engineers, create reusable tooling, communicate findings to diverse stakeholders, and mentor data scientists. The role combines hands-on data science with technical consulting and requires occasional customer on-call support.

Lead data science strategy for newsroom audience analytics by building predictive and explanatory models, metrics, forecasts, and measurement systems. Partner with editorial, audience, product, and digital strategy teams to guide content and engagement decisions. Apply causal inference, experimentation, and statistical methods; translate findings into recommendations; maintain production analytics workflows; and mentor junior data scientists while advancing technical standards.

Develops statistical and machine learning models, analyzes complex datasets, builds trustworthy data assets, and generates insights that support business growth and customer behavior analysis. The role collaborates with stakeholders, data engineers, and architects; leads high-impact projects; informs experimental design; optimizes model performance; and mentors colleagues in data science and machine learning.

Lead experimentation, causal inference, advanced analytics, machine learning, and Generative AI initiatives. Design and analyze marketing and product experiments, build scalable predictive and prescriptive models, establish statistical governance, develop data pipelines, influence executive stakeholders, and mentor data scientists. Translate complex business challenges into measurable analytical solutions across marketing, product, sales, risk, and compliance functions.