Top Data Science Jobs

Summer 2027 internship supporting Sector Data Scientist and investment teams by acquiring, cleaning, and analyzing multiple data sources, testing investment hypotheses, identifying key data drivers and inflection points, and presenting research and decision-support tools to inform investment decisions.

Build and deploy pricing, underwriting, and risk-scoring models for commercial property and general liability insurance. Use bind, quote, loss, and exposure data to identify profitable segments, predict carrier pricing, improve underwriting guidelines, and monitor model performance. Partner with program leadership to translate model outputs into actionable underwriting decisions and recalibrate models as the business grows.

Owns marketing and growth measurement, including attribution, incrementality, CAC, LTV, ROAS, experimentation, and budget optimization. Builds predictive models, data models, dashboards, reporting, and lightweight pipelines using product, advertising, behavioral, and on-chain data. Partners with Growth, Marketing, Product, and Engineering to improve acquisition performance, data infrastructure, and user-value understanding.

Develop and deploy complex machine learning and analytics models for client systems. Manage large datasets, perform data ingestion, cleaning, feature extraction, modeling, validation, visualization, and reporting. Collaborate with clients and distributed data science and systems engineering teams while delivering maintainable, tested, version-controlled, cloud-based solutions within cost and schedule constraints.

The Senior Data Scientist develops and operationalizes predictive models and AI algorithms across investment platforms. Responsibilities include data analysis, feature engineering, model experimentation, deployment with MLflow and Docker, CI/CD automation, monitoring for drift and performance, data pipeline and lake design, governance, security, compliance, and stakeholder collaboration. The role requires translating technical findings into strategic recommendations and managing the full machine-learning lifecycle.

Analyze product data and develop dashboards, pipelines, models, and A/B tests to improve Roku’s Home screen and discovery products. Provide actionable insights, identify trends and opportunities, guide product strategy, monitor key metrics, and influence leadership decisions. Collaborate with product, engineering, marketing, and other stakeholders to drive adoption, efficiency, and measurable business outcomes. Use AI and large language models to enhance analytical workflows.

Leads enterprise data architecture and advanced data science for the Sentinel program. Designs scalable, secure data platforms and big-data pipelines; develops statistical, machine-learning, and deep-learning solutions; establishes standards for model validation, reproducibility, observability, and deployment. Translates data strategy into executable architecture, mentors technical teams, influences modernization and adoption of emerging technologies, and advises senior leadership, customers, and multidisciplinary stakeholders.

Develop scalable operations research and data science solutions using optimization, simulation, machine learning, and information retrieval. Partner with technical and business teams to frame problems, build production-ready analytical models, evaluate performance, recommend technical roadmaps, and deliver actionable insights. Lead data science practices, communicate recommendations, improve model quality, and establish long-term processes, frameworks, tools, and standards.

Lead customer-facing data science for major advertisers, advising on campaign structure, measurement, bidding, and optimization. Translate advertiser insights into product roadmap priorities, define activation frameworks, design experiments, evaluate product features, and establish scalable global playbooks. Partner with product, engineering, sales, and go-to-market teams across regions while communicating complex analysis to technical and executive stakeholders.

Lead data science for a core fintech product area, defining metrics, experimentation strategies, causal analyses, and models. Partner with product, engineering, design, and marketing to guide roadmap decisions. Build AI product evaluations, automated scorers, regression tests, and model monitoring practices. Contribute to company-wide data science standards, AI-enabled workflows, and measurement frameworks while mentoring teammates and communicating recommendations to senior audiences.

Develops advanced data science and machine learning methods for large, complex NSA/CSS datasets. Responsibilities include data processing, statistical and mathematical modeling, predictive analysis, programming, workflow reproducibility, translating mission needs into technical requirements, evaluating technical solutions, and communicating findings to technical and nontechnical audiences.

Develops and applies data science, machine learning, predictive modeling, and big data techniques to analyze large datasets. Designs data-handling, search, storage, and processing approaches; develops software to improve data searches; validates data quality and processes; and delivers actionable recommendations. The role emphasizes scientific modeling and complex data challenges over visualization and traditional analytics.

Analyze complex structured and unstructured data; build and maintain data pipelines; develop predictive, prescriptive, NLP, clustering, forecasting, and statistical models; process data using cloud platforms and distributed frameworks; automate data workflows; create visualizations; collaborate with data engineers; and communicate findings to technical and nontechnical stakeholders supporting a U.S. Special Operations Command program.

Lead end-to-end data science initiatives, including exploratory analysis, feature engineering, model development, deployment, and monitoring. Develop scalable solutions for forecasting, fleet planning, maintenance, vehicle operations, and customer satisfaction. Partner with business teams, create leadership-ready data stories, develop MVPs, and mentor junior analysts. The role requires strong Python and SQL skills, machine learning experience, distributed data processing expertise, and the ability to translate complex analyses into practical business decisions.

Develop and migrate reporting assets from SAP BusinessObjects to QlikView and Qlik Sense. Analyze and rebuild Web Intelligence, Crystal Reports, and universes; debug legacy and new reports; optimize SQL, scripts, expressions, data models, and load processes; validate output accuracy; document migration logic; and collaborate with stakeholders on requirements and report specifications.

Lead development and productionization of telematics pricing and underwriting models. Responsibilities span statistical modeling, feature engineering, dataset and pipeline development, model deployment, monitoring, data quality validation, troubleshooting, regulatory support, and collaboration with pricing, actuarial, product, engineering, and compliance teams.

Lead data science, data engineering, and applied AI as the program’s senior technical authority supporting DIA analysts. Establish responsible and auditable AI standards, guide analytic tools from pilot through deployment in classified environments, mentor technical staff, develop training, coordinate with government and partner teams, support hiring, and remain hands-on in data science and tool development.

Leads consumer personalization strategy and production development for recommendations, ranking, search, browse, carts, marketing, and agentic AI experiences. Builds recommender systems, learning-to-rank models, LLM agents, evaluation frameworks, and experimentation programs. Partners with Product and Engineering on deployment, monitoring, and model quality while mentoring senior data scientists, setting technical direction, shaping roadmaps, and communicating trade-offs to executives.

Conduct research and develop innovative data science and structured analytic solutions for Department of Defense intelligence challenges. Apply analytic methodologies, coding, data visualization, intelligence tools, and emerging technologies to operational data. Collaborate with analysts, engineers, and designers to create applied capabilities, document findings, and communicate results through written, verbal, and multimedia presentations. The role is on-site in Tampa and requires a Top Secret clearance with SCI eligibility.

Turn data into product and business decisions through metric design, experimentation, causal analysis, data modeling, deep-dive analysis, dashboards, and data storytelling. Partner with Product, Finance, GTM, Growth, and Engineering leaders to measure impact, influence priorities, and improve company-wide data practices. Model analytical data in dbt, collaborate with data engineering, and help shape an AI-first data team.

Build statistical and machine learning models that improve content discovery, user engagement, and retention. Apply causal inference, experimental design, and A/B testing to measure product impact and validate changes. Collaborate with ML engineers to deploy models, translate analysis into product decisions, and develop features using text, multimodal embeddings, and GenAI. Analyze large-scale data and recommender systems to drive measurable improvements in a consumer product serving millions of users.

Leads end-to-end machine learning model development, from business problem definition and data exploration through deployment, monitoring, documentation, and retraining. Mentors data scientists, collaborates with stakeholders and product teams, troubleshoots production code and ML pipelines, and guides Agile delivery. Requires advanced expertise in statistical modeling, machine learning, cloud platforms, MLOps, production coding, large datasets, containers, version control, and data lakes.

Develop and maintain data analytics solutions, predictive models, and machine learning algorithms to improve LNG production efficiency and reduce safety and downtime risks. Analyze data lake and warehouse data, define ETL and architecture requirements, support governance and testing, collaborate with business stakeholders, document solutions, and mentor junior data scientists. The role also contributes to data science lifecycle standards, technology stack development, and strategic planning.

Develop, deploy, and monitor predictive models and algorithmic decision systems for automated sports pricing and market making. Prepare data, build backtesting and simulation tools, analyze model and trading performance, and improve quoting, pricing, exposure management, and execution quality. Create dashboards and diagnostics while collaborating with traders, quantitative engineers, product, and engineering teams. The role includes occasional evenings, weekends, and holidays aligned with major sporting events.

Develop algorithms, models, and analytical systems to transform complex structured and unstructured datasets into actionable insights. Collaborate with clients to define questions, gather and clean data, apply statistical and scientific methods, and support decisions across areas such as fraud detection, cancer research, and national intelligence. The role requires R or Python, advanced quantitative skills, and an active TS/SCI clearance with polygraph.