Top Data Science Jobs

Lead predictive modeling and advanced analytics initiatives supporting revenue growth, customer engagement, and sales effectiveness. Develop analytical workflows, features, and scalable data pipelines; perform statistical analysis on large datasets; and deploy, monitor, and validate production models. Partner with Sales, Marketing, Product, Partner, and Customer Success teams to translate business questions into data science solutions. Communicate insights through documentation, dashboards, and presentations while supporting senior data scientists on complex AI initiatives.

Design and deploy predictive models and reproducible pipelines for voter and survey data. Build data infrastructure, automate workflows, develop tools and internal platforms, apply ML and AI for survey analysis, and communicate insights to non-technical stakeholders to inform political and policy strategy.

Leads advanced data science research and technology development across complex datasets and business areas. Designs algorithms, analytics, visualization, data integration, and machine learning solutions; generates and leads scientific proposals; evaluates emerging technologies; applies pharmaceutical and healthcare expertise; publishes and presents findings; and may mentor team members while ensuring compliance with corporate, GxP, safety, and data security standards.

The Data Scientist will develop agent-based workflows and AI features for CAS products, emphasizing software development and collaboration with teams.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Build and maintain forecasting, driver-based models, and analytics for FP&A. Analyze historical and operational drivers, improve forecast accuracy with statistical and ML methods, create dashboards and automated reports, integrate ERP/CRM data, perform variance and scenario analysis, and present insights to finance leaders.

Lead development of data science and ML solutions: explore, clean, analyze structured and unstructured data, build predictive models, create visualizations, guide teammates, and advise clients—often within secure DoD environments.

Work with clients to analyze large, structured and unstructured data sets, design and test AI/ML models, mentor teammates, and deliver actionable insights to leadership, primarily supporting DoW/IC customers.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; build centralized experimentation frameworks; extract and analyze data with SQL and Python; mentor data scientists and communicate results to stakeholders and senior leadership.

The Senior Data Scientist develops machine learning and AI solutions, prepares structured and unstructured data, builds dashboards and BI applications, and translates operational challenges into actionable insights. The role partners with Biologics Operations stakeholders, leads AI pilots and proof-of-concepts, supports reporting tools, mentors junior staff, and promotes Agile delivery, collaboration, and responsible technology adoption in a regulated manufacturing environment.

Build and deploy predictive models that drive partner action, analyze conversational and unstructured data using NLP techniques, and monitor model performance in production. Own AI data tooling, evaluation frameworks, in-warehouse model configuration, and reusable analytics foundations. The role also establishes modeling standards, evaluates fairness and limitations, collaborates with engineering and product teams, and prioritizes high-impact work across a lean data organization.

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 advanced analytics, statistical models, machine learning, and GenAI solutions for Microsoft’s sales, marketing, and support platforms. Translate business needs into measurable problems, create implementation plans, write reusable modeling code, and deliver statistically reliable results. Lead GenAI solution evaluations, diagnose root causes, and recommend fine-tuning or reinforcement-learning improvements. Collaborate with data scientists, engineers, product managers, and stakeholders to drive product insights and AI transformation.

Leads enterprise manufacturing AI, machine learning, and Industry 4.0 initiatives across multiple plants. Develops and deploys solutions for predictive maintenance, process and quality optimization, energy efficiency, throughput, and OEE improvement. Establishes scalable data and AI platforms, governance, and MLOps standards while integrating industrial data sources. Leads and mentors data science teams, prioritizes projects, allocates resources, partners with cross-functional manufacturing stakeholders, and presents strategic recommendations and business cases to executives.

Senior Data Scientist partnering with Product, Engineering, and Design teams to analyze user behavior, product performance, and marketplace dynamics. Responsibilities include developing metrics and reporting frameworks, designing experiments, diagnosing business trends, analyzing marketplace behavior, and aligning stakeholders on analytical priorities. The role uses SQL and Python alongside BigQuery, dbt Cloud, Airflow, Tableau, and Statsig, with a focus on product analytics, experimentation, and data-informed decision-making.

The Principal Data Scientist will analyze large datasets, develop metrics systems, dashboards, business models, and A/B tests to improve MSN content relevance, recommendation performance, user engagement, and business growth. The role partners with cross-functional stakeholders, identifies trends and root causes, evaluates product performance, recommends improvements, and communicates actionable insights to technical and non-technical audiences.

Lead ecosystem-level data science and econometric analyses across Xbox, Game Pass, Console, PC, and Store experiences. Evaluate growth, engagement, monetization, experiments, incrementality, and investment ROI using statistical and causal inference methods. Build reusable analytical products and self-service capabilities, influence executive decisions, and mentor team members.

Develop machine learning and AI methods for cybersecurity products using large-scale network telemetry. Analyze anomalies and malicious behavior, engineer features, evaluate models on imbalanced and incomplete data, establish monitoring metrics, and conduct error analysis. Collaborate with threat researchers and software engineers to productionize solutions, research emerging techniques, communicate findings, document methodologies, and mentor data scientists.

Designs, builds, and sustains data architecture, pipelines, ETL processes, and decision-support products for USPACOM’s MRT-C baseline. Ensures compliance with DoD data standards, Azure requirements, classification controls, and cross-domain information sharing needs. Integrates and validates terrain data, conducts quality assurance, produces baseline reports, and supports resource proposal development. The role requires advanced computational, software development, data analysis, machine learning, and cloud technology expertise in a national security environment.

Analyze complex operational, program, schedule, requirements, risk, and workstream data to create trusted metrics, dashboards, models, and decision-support products. Define KPIs and analytical logic, assess data quality, develop Python/R/SQL analyses, and support AI-enabled workflows. Partner with architects, engineers, leadership, and mission stakeholders to translate ambiguous operational questions into measurable insights and recommendations for FAA modernization and Digital Command Center initiatives.

Develop and validate predictive and prescriptive machine learning models for cyber defense decision-making. Perform statistical analysis, anomaly detection, event correlation, and operational research to identify trends and characterize threats. Translate mission requirements into analytic solutions, document methodologies, and mentor junior data scientists. The role requires advanced Python, R, and SQL skills, model deployment experience, strong statistical expertise, and a Top Secret clearance with the ability to obtain and maintain SCI eligibility.

Develop AI tools, machine learning systems, predictive analytics, data visualizations, and large-scale Multi-INT data fusion capabilities for USCYBERCOM J2 intelligence operations. Analyze and merge disparate data using R, Python, and SQL; create Power BI and Tableau visualizations; produce analytic reports; and collaborate with intelligence stakeholders to prioritize capability gaps.

Analyze complex structured and unstructured data; design data pipelines; develop predictive, prescriptive, NLP, clustering, forecasting, and machine learning models; automate data processing; use cloud and distributed computing platforms; create visualizations; collaborate with data engineers; research algorithms; and communicate insights to nontechnical audiences supporting U.S. Special Operations Command.

Leverage complex structured and unstructured data to support U.S. Special Operations Command decision-making and digital transformation. Design and maintain data pipelines, perform statistical analysis, develop predictive and prescriptive models, and implement machine learning, NLP, forecasting, clustering, and text-processing solutions. Use cloud data platforms and distributed processing frameworks to integrate, analyze, and visualize data. Collaborate with data engineers, communicate findings to nontechnical audiences, and support mission readiness initiatives.