Top Data Science Jobs

Develop predictive models and analytical solutions for entity resolution using large, diverse datasets. Prepare training data, build and test algorithms, conduct A/B testing, visualize findings, and support deployable solutions. Apply machine learning, data modeling, cloud technologies, and GCP tools to improve analytics workflows and automation. Collaborate with global stakeholders and data science teams while evaluating peer work and communicating insights to management.

Leads data science initiatives by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Develops predictive models, quantitative analyses, machine learning and NLP solutions, and algorithms that inform client decisions. Guides teammates, leads complex solution development, and works with clients to translate data into actionable insights. May support software integration, testing, scripting, ETL, APIs, and advanced analytics using Python, R, SQL, and related tools.

Develop and optimize data-driven marketing strategies for credit card products. Responsibilities include campaign analytics, modeling, experimentation, funnel and ROI analysis, direct mail campaign execution, KPI definition, dashboard development, data pipeline validation, and performance monitoring. The role partners with marketing, product, business leaders, and external stakeholders to identify growth opportunities, communicate insights, and drive profitable member acquisition and retention.

Develop and productionize data and AI solutions across the full machine learning lifecycle, including problem identification, model design, training, deployment, monitoring, and impact measurement. Partner with Operations, Engineering, Product, Fraud, Compliance, and Credit teams to manage risk and improve customer experience. Apply statistical techniques, build APIs, integrate ML systems into production services, and collaborate with technical and non-technical stakeholders.

Researches, designs, implements, and deploys machine learning algorithms for enterprise applications and federal customers. Collaborates with data scientists, software engineers, and subject matter experts to develop analytics capabilities, support application development, and deliver scalable solutions. Requires strong Python programming, applied machine learning, mathematical expertise, communication skills, and the ability to manage concurrent projects independently and collaboratively. Cloud, deep learning, NLP, computer vision, and reinforcement learning experience are desirable.

Partner with Product, Engineering, and Go-To-Market teams to define success metrics, design experiments, evaluate product launches, analyze large-scale ad-tech data, and guide roadmap decisions. The role requires translating complex data science findings into actionable recommendations for technical and non-technical stakeholders while applying rigorous statistical, modeling, and experimentation practices.

Develops statistical, machine learning, deep learning, and NLP solutions for legal and administrative teams. Responsibilities include building regression and classification models, fine-tuning language models, creating RAG pipelines, designing AI workflows, analyzing structured and unstructured data, producing visual reports, and deploying reproducible production systems. The role collaborates with lawyers, business departments, and technical teams to advance the firm’s AI capabilities and deliver actionable insights.

Leads development of intelligence-focused analytics products, integrating structured and unstructured data into analytic workflows. Performs quantitative and qualitative intelligence analysis supporting operations and exercises, provides onsite subject-matter expertise during command exercises, and supports senior leaders through executive briefings and report writing. The role requires strong intelligence reporting skills, Indo-Pacific national security expertise, and an active Secret clearance with eligibility for Top Secret.

Develop valuation and forecasting models, including customer lifetime value, net present value, and offer economics, to guide marketing investments. Analyze customer, offer, channel, and marketing data; perform data preparation, exploratory analysis, modeling, validation, experimentation, and performance monitoring. Partner with marketing, finance, and data teams to communicate insights and support data-driven recommendations.

Conduct quantitative labor market research for clients by querying, cleaning, and analyzing large datasets with SQL, Python, and Snowflake. Build data pipelines, indices, repeatable workflows, dashboards, and client-ready materials. Apply statistical methods and labor economics concepts, present findings, use LLM tools responsibly, document methodologies, and collaborate on research, product work, proposals, and client deliverables.

Conduct statistical analysis, causal inference, experimentation, predictive modeling, and machine learning using large datasets. Build data pipelines, dashboards, reports, and production models with Python or R, SQL, and Spark. Analyze fraud, financial crime, and business outcomes, communicate actionable recommendations, collaborate with multidisciplinary teams, and present findings to stakeholders.

Build and deploy predictive, forecasting, pricing, promotion, and machine learning models using large-scale retail data. Apply statistical modeling, causal inference, and experimentation to support demand forecasting, inventory planning, merchandising, and supply chain decisions. Partner with Product, Engineering, Analytics, and Business teams to define requirements, evaluate model performance, and translate complex findings into actionable recommendations and production-ready solutions.

Leads advanced GEOINT exploitation across EO, IR, SAR, multispectral, and specialized sensors. Develops machine-learning and statistical models for detection, classification, change analysis, and data fusion; evaluates sensor performance; designs reproducible data workflows; integrates AI capabilities; delivers technical briefings and intelligence products; establishes analytic standards; mentors staff; and collaborates with government and intelligence stakeholders.

Lead client engagements from data onboarding through production, plan studies that demonstrate business value, and build automated machine learning workflows using Python, scikit-learn, pandas, and Spark. Translate client business questions into data solutions, manage stakeholder relationships, evaluate model performance, and inform product planning. Requires healthcare or life sciences data experience and end-to-end delivery of sophisticated data or machine learning solutions.

Develop machine learning and data science solutions for financial crime risk management. Responsibilities include analyzing large datasets with SQL, Python, and PySpark; designing ETL pipelines; training supervised and unsupervised models; implementing solutions in Scala; applying LLMs and NLP; supporting customer proofs of concept; and collaborating with product and platform teams in an agile environment.

Analyzes complex structured and unstructured data, develops statistical, predictive, prescriptive, NLP, and machine learning models, and builds data pipelines and automated processing environments. The role uses Python, R, distributed data technologies, cloud platforms, and visualization tools to support U.S. Special Operations Command decision-making, digital transformation, and mission readiness. It also requires communicating analytical findings to nontechnical stakeholders and collaborating with data engineers and command personnel.

Lead homeowners insurance loss-cost modeling, including frequency, severity, demand, retention, conversion, and aggregation models. Design and deploy predictive models using Python, SQL, and Airflow-based workflows, ensuring testing, validation, reproducibility, and maintainability. Partner with product, underwriting, and engineering teams to translate actuarial insights into pricing and portfolio decisions while developing reusable analytical tooling and supporting team standards.

Build and deploy predictive models for workforce attrition, turnover, hiring, and headcount forecasting. Develop AI agents using sensitive People data, LLMs, tool orchestration, and guardrails. Apply experimentation, causal inference, and uncertainty quantification; monitor model performance and collaborate on data pipelines. Partner with HR leaders, Workday, Visier, and Data Engineering, while explaining models and agent behavior to nontechnical audiences.

Prepares laboratory and commercial data for statistical, hybrid, mechanistic, and machine-learning models supporting product development and manufacturing sciences. Builds data pipelines, enables model execution, feature-engineers datasets, supports digital twins, and partners with Digital and scientific teams. Conducts autonomous research, designs experiments, develops scientific conclusions, and contributes to publications, regulatory documents, and patents while maintaining GxP, safety, and corporate standards.

Supports the design, testing, calibration, and deployment of predictive risk models for Naval safety. Analyzes complex datasets using time series analysis, forecasting, statistical modeling, causal inference, anomaly detection, feature engineering, and machine learning. Develops models in Python or R and collaborates with data scientists and software engineers to transition analytical solutions into operational environments.

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Design and deliver analyses, reports, and dashboards; translate technical findings for business leaders; perform complex data queries and reconciliation; identify data capabilities and partner with enablement teams; mentor analysts and drive a data-driven culture across functions.

Applied Data Scientist focused on incrementality, causal inference, experimentation, Marketing Mix Modeling, and predictive products. The role partners directly with customers to interpret data, diagnose measurement issues, explain statistical results, and provide actionable recommendations. Responsibilities include building diagnostic tools, analyzing model and experiment outcomes, improving methodologies and production capabilities, and collaborating with Product, Engineering, Media Strategy, Customer Success, and senior data scientists.

Conduct research and predictive modeling for Citi Bike and shared mobility programs; build Power BI dashboards, reports, and briefing materials; translate complex data into actionable recommendations; monitor vendor compliance and data quality; document data procedures; maintain internal databases; and collaborate with Operations, Planning, Policy, leadership, and other stakeholders.

Develop member-level predictive models and longitudinal analyses for CMS Health Outcomes Survey measures and Medicare Advantage Star Ratings. Analyze survey drivers, trajectories, sampling, response rates, case-mix adjustments, and statistical significance. Build data pipelines and dashboards, validate methodologies, deploy models with engineering teams, and translate findings into clinical and operational interventions. Collaborate with Stars, clinical, care management, compliance, legal, and survey vendor stakeholders to ensure CMS-aligned outcomes and reporting.