Top Data Science Jobs

As the first Data Scientist, you will build models and algorithms from scratch, impacting the company's success in optimizing insurance portfolios.

Design, run, and analyze experiments (A/B/n) to optimize personalization, retention, and loyalty for Sportsbook marketing. Partner with marketing, ML engineering, and data teams to advance experimentation tools, automate analyses, and present actionable insights to stakeholders to inform promotional strategy.

Design and implement data science solutions: prepare and transform diverse data, build data pipelines and algorithms, perform statistical modeling and large-dataset querying, create visualizations and proofs-of-concept, and support requirements for dashboards and analytical products.

Lead advanced analytics and modeling efforts: develop queries, prepare data, design and validate predictive and prescriptive models, mentor junior staff, engage stakeholders, deliver insights and recommendations, and apply modern data science and big-data techniques across business functions.

Lead data science initiatives for pricing and demand forecasting using deep learning and time series methods. Design and productionalize models, communicate insights to business leaders, mentor junior data scientists, manage project priorities, and collaborate cross-functionally to drive profitable pricing decisions and scalable analytics solutions.

Lead development and operationalization of data-driven financial forecasting (time-series, AI/ML, causal, Monte Carlo). Integrate statistical outputs into Anaplan planning, establish model governance/validation, build templates/dashboards, collaborate with FP&A and Data/IT, and create a Center of Excellence for advanced forecasting and repeatable playbooks.

Develop, validate, and deploy predictive and statistical models on large tax and financial datasets to detect fraud and non-compliance. Perform data exploration, feature engineering, and model evaluation using Python/R, SQL, and Databricks. Collaborate with government clients and cross-functional teams, deliver technical and non-technical briefings, and ensure models are explainable, defensible, and maintainable in secure government environments.

Lead Data Scientist will develop, validate, deploy, and monitor predictive ML models, design scalable data pipelines, perform advanced SQL analysis, partner with stakeholders to translate business needs into production-ready solutions, and contribute to data science best practices and governance.

Lead cross-functional data science initiatives for marketing, growth, and product. Design causal measurement and experimentation frameworks, build ML and uplift models for personalization and allocation, improve data quality and instrumentation, use AI-assisted tools to accelerate analysis, and mentor other data scientists while delivering clear, executive-level recommendations.

Lead experimentation and causal inference at company-wide scale: define methodology, own statistical approaches for small samples and clustered data, apply causal methods when experiments arent feasible, run experiment review, build tooling and curriculum, and partner with analytics and AI/ML teams to ensure decision-grade results.

Develop ETL pipelines, datasets, dashboards, predictive and optimization models to support fleet planning, procurement, lifecycle and residual value analysis. Cleanse and analyze vehicle, supplier, telematics, maintenance, and financial data, communicate findings to stakeholders, and support cross-functional delivery and adoption of analytics solutions.

Lead marketing measurement strategy with MMM and MTA; design incrementality and experiment frameworks (geo experiments, lift studies); interpret modeling results for stakeholders; provide technical leadership in marketing data science and ensure privacy/HIPAA-compliant solutions for healthcare marketing.

Lead data science efforts to design experiments, build statistical and ML models (audience segmentation, bidding, LTV, attribution), develop time-series and real-time event understanding, productionize models with engineering, and communicate insights to inform product and commercial strategy.

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.

The Senior Associate Scientist will analyze and visualize data, collaborate on screening strategies, and validate hits for drug discovery using the EMMI platform.

The Senior Data Scientist will develop analytical solutions for marketing and risk management, manage data sets, and support modeling activities, collaborating with teams to deliver actionable business insights.

Lead data science for fraud and risk analytics: translate business challenges into metrics, analytics, experimentation, and BI; build, productionize, and enhance models and decisioning frameworks; run statistical analysis and A/B tests; collaborate with product, finance, compliance and legal; communicate insights to senior leadership; and champion data quality and accessibility.

Apply advanced data science and ML techniques to national security problems: curate, transform, analyze large SIGINT/RF/PROFORMA datasets; build reproducible analytics, models, and visualizations; automate workflows and optimize data pipelines; communicate results to technical and non-technical stakeholders.

Client-facing data scientist who builds and optimizes fraud detection models and policies using large, multi-product datasets. Scope and deliver customer analytics projects, drive research and innovation, present insights to non-technical audiences, and collaborate with product and engineering teams. Use SQL, Python, LUA, BI tools, Git and Jira to create scalable solutions and become a subject-matter expert in cybercrime and industry-specific fraud patterns.

Design, build, and deploy predictive models for risk and fraud using R, Python, SAS, and SQL. Analyze large datasets, develop cloud-based analytic solutions, validate scoring code with stakeholders, and document statistical methodologies and best practices.

Lead data science strategy across complex customer engagements, turn ambiguous business problems into repeatable ML solutions, define modeling and evaluation standards, drive data readiness and MLOps practices, mentor teams, produce reusable IP, and represent Microsoft in executive and thought-leadership forums.

Lead product and program analytics for Microsoft skilling experiences: design learner journey/funnel analyses, define success metrics, run experiments and causal analyses, build proficiency/competency models, shape telemetry and scalable reporting, recommend content and curriculum improvements, and mentor other data scientists to drive product decisions and learning outcomes.

Collect, integrate, and prepare data from multiple sources; design data models, predictive algorithms, and ML solutions; build dashboards and visualizations; ensure data quality and support analytics for FBI financial management, client delivery, and business development.

Analyze large complex datasets, build predictive models and A/B tests, create visualizations and dashboards, collaborate with engineers and stakeholders to improve data quality and support data-driven decisions, and stay current on data science best practices.

The Data Scientist III will enhance cybersecurity operations using data science and machine learning techniques, developing analytical models, and integrating data from various sources to improve threat detection and monitoring.