Top Data Science Jobs

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.

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.

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 the research, development, and production deployment of advanced machine learning, deep learning, forecasting, generative AI, and agentic systems. Fine-tune LLMs, design conversational interfaces, and build scalable AI capabilities for enterprise planning applications. Collaborate with Engineering, Product, and Design to move solutions from prototypes into production while mentoring data scientists and engineers and driving technical excellence.

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.

Develop machine learning and statistical models using chemical, materials, and experimental data to support formulation design, process optimization, and product performance prediction. Build reproducible data pipelines and analytical tools, integrate experimental and simulation data, collaborate with R&D and business teams, and communicate data-driven recommendations to technical and non-technical stakeholders.

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.

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.

Lead complex data science projects using statistical analysis, predictive modeling, machine learning, data mining, and visualization. Develop models and algorithms, assess data quality and model performance, communicate actionable findings to technical and executive stakeholders, and provide mentorship to other data scientists. Partner with multidisciplinary teams to improve operations and manage multiple priorities.

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.

Develops data-driven solutions for government and strategic customers by acquiring and preparing datasets, evaluating and improving statistical models, building scalable operational models, researching industry trends, and collaborating with software engineers, technical program managers, designers, and customers on cloud-based AI projects.

Develop data foundations, schemas, ontologies, and AI-enabled tools for fusion hardware systems. Analyze telemetry, experimental, and time-series data to support operators, detect anomalies, diagnose hardware behavior, and improve engineering decisions. Build evaluation frameworks, data pipelines, analytics-ready datasets, and AI workflows involving retrieval, summarization, semantic search, and anomaly analysis while collaborating with engineering, science, and operations teams onsite in Everett, Washington.

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.

Lead ecosystem-wide measurement strategy for Microsoft AI products. Define metrics, experimentation frameworks, causal-inference analyses, and evaluation systems that guide product strategy, investment, and resource allocation. Partner with product, engineering, business, and executive leaders to identify opportunities, quantify impact, and align organizations. Provide technical leadership, mentorship, and analytical best practices across large-scale data science and experimentation initiatives.

Design, deploy, and optimize production machine learning and data science solutions across the full lifecycle. Develop predictive models, analytical pipelines, and scalable machine learning applications; monitor model performance; and translate business challenges into actionable insights. Collaborate with engineers, developers, product managers, stakeholders, and cross-functional Agile teams while working with large structured and unstructured datasets, cloud platforms, data warehouses, and distributed processing technologies.

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 end-to-end data science projects to build, validate, and deploy statistical and machine learning models. Drive experimentation, causal analysis, and decision-support analytics for product and business stakeholders, mentor junior team members, and partner with engineering to productionize models and measurement frameworks.

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.

Senior Data Scientist supporting Slack’s GTM organization through advanced analytics, experimentation, predictive modeling, forecasting, and measurement. The role identifies growth opportunities, evaluates strategic initiatives, develops analytical frameworks, and translates complex findings into executive recommendations. It partners with Sales, Product, Finance, Data Engineering, and leadership teams to improve customer adoption, revenue growth, operational efficiency, and evidence-based decision-making.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence leadership decisions. The role requires independently structuring data, solving ambiguous problems, communicating findings clearly, and driving recommendations through execution.

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 product analytics and experimentation for Yahoo’s homepage. Defines product metrics, designs and interprets complex A/B tests, applies advanced statistical methods, and delivers insights influencing product strategy. Builds dashboards, scalable data assets, datasets, and instrumentation with Data Engineering. Uses AI-assisted tools to automate analytics workflows while validating generated code and queries. Partners cross-functionally with Product, Engineering, and Design, and mentors junior analysts while promoting rigorous experimentation and data quality practices.

Own end-to-end data science for a business unit across growth, product, AI and automation, or strategic partnerships. Define metrics, build reports, analyze data, design experiments, identify insights, and influence leadership decisions. The role requires independently structuring ambiguous problems, communicating recommendations clearly, and driving measurable outcomes through execution.