Top Data Science Jobs

Design and implement statistical, ML, and geospatial models to extract actionable intelligence from large structured and unstructured datasets. Perform EDA, feature engineering, model evaluation, and operationalization on cloud-native secure platforms. Produce visualizations, dashboards, documentation, and collaborate with engineers, architects, and mission stakeholders to gather requirements and integrate analytical capabilities into operational systems.

Build and deploy credit risk models for acquisition and account management. Create datasets and features from traditional and non-traditional sources, collaborate with credit, product, and engineering partners, run experiments, and monitor models in production to enable responsible lending decisions.

As a Data Scientist, you will generate insights to prioritize efforts and identify opportunities, focusing on experimental and causal inference methodologies.

The Data Scientist will assist in developing AI and data science solutions, focusing on data analysis, model development, and collaborating with cross-functional teams, while under supervision and mentorship.

Use data science to source, ingest, clean, transform, analyze, and visualize structured and unstructured data in WDP. Develop dashboards and data products (Databricks, SQL, Python, Tableau/Qlik), create visual narratives, and brief senior DoW leadership to support IT portfolio management.

Lead data science efforts: explore, clean, analyze structured and unstructured data; design data pipelines; build predictive models and visualizations; guide teammates and advise clients. Communicate findings at strategic and technical levels.

Perform data science for defense intelligence: collect/clean data, build statistical and ML/NLP models, code in Python/SQL on AWS, produce dashboards/reports (Tableau), and support analysts. Work independently on moderate-complexity problems; active TS/SCI with polygraph required.

Data Scientists develop and maintain AI software and systems, focusing on machine learning algorithms, model deployment, and data analysis for predictive maintenance and failure prediction.

Lead company-level measurement and causal strategy, define MAU×ARPU metrics and semantic mart, set scientific standards for experimentation and analysis, own strategic models (MMM, elasticity, forecasting), partner with engineering to productionize analytics, and raise org-wide scientific maturity through governance, templates, and upskilling.

Build and deploy machine learning and experimentation systems for personalization, ranking, targeting, attribution, and causal measurement. Develop features and pipelines from transaction and behavioral data, design A/B tests, productionize models with engineering teams, and improve recommendation systems. Translate analytical results into business insights tied to conversion, retention, lift, and ROI while owning projects from problem framing through post-launch evaluation.

Develop and enable clinical data assets for analytics and AI/ML use cases. Responsibilities include designing clinical data models, taxonomies, feature stores, documentation, quality frameworks, dashboards, queries, and reusable datasets. Partner with clinical, business, engineering, and analytics stakeholders; support data governance and HIPAA compliance; translate clinical concepts into analytical frameworks; and train data consumers. Requires expertise in healthcare datasets, coding systems, SQL, Python, cloud platforms, and clinical informatics.

Build and evaluate predictive models for complex transactional data, using advanced SQL, Python, cloud data infrastructure, and statistical methods. Trace data quality issues through ETL and CDC pipelines, communicate findings to senior leaders and nontechnical stakeholders, and use AI tools to accelerate analysis. Preferred work includes LLM, RAG, BI, data testing, vector database, and commercial real estate applications.

Contribute to data pipelines, validate and refine analytical models, perform advanced analyses and data quality checks, collaborate with cross-functional teams, and write maintainable analytical code and documentation to support business decision-making.

Perform data science on classified datasets: build and evaluate ML/AI models (including LLMs/agentic AI), clean and retrieve data, visualize findings for non-technical leaders, and support DoW/IC clients while holding required security clearance.

Designs, develops, validates, implements, and monitors statistical and machine-learning solutions for insurance pricing, product development, and profitable growth. Owns analytical workstreams from problem definition through delivery, builds scalable workflows using Python and cloud technologies, collaborates with data and business teams, participates in model and code reviews, communicates recommendations to stakeholders, and provides technical guidance to less experienced data scientists.

Leads big data and analytics projects by integrating diverse data sources, building and validating predictive and statistical models, identifying trends, and delivering actionable recommendations. Uses advanced statistical and machine learning techniques to answer business questions, communicates findings clearly, collaborates with stakeholders, monitors outcomes, and ensures data and analytics practices comply with company standards.

Lead data science for Disney’s News & Entertainment Machine Learning Platform. Conduct exploratory analysis, design and evaluate A/B tests, define success metrics, assess model performance, and partner with ML engineers on features and drift detection. Translate behavioral and content insights into product recommendations and roadmap priorities. Analyze large-scale datasets using Python, SQL, and PySpark, communicate findings to technical and non-technical stakeholders, and support personalization products across Disney media brands.

Conduct exploratory data analysis, run and interpret A/B tests, define and track product metrics, and support machine learning feature analysis and model evaluation. Analyze user, content, and engagement data using Python, SQL, and PySpark; monitor model performance and identify anomalies. Collaborate with product, design, engineering, and data science partners, communicating findings and recommendations to inform product decisions.

Build full-stack data science solutions for finance: develop, test, and ship models; partner with data engineering and finance teams to create actionable financial insights; drive experimentation and analytics best practices; produce dashboards and reports to influence business decisions.

Lead and mentor a data science team to design, build, and deploy advanced analytics and AI/ML solutions (including LLMs/agentic AI) for classified DoD/IC clients. Translate complex data into visual and written narratives, advise leadership on risks and go/no-go criteria, and identify new data-driven opportunities. Work includes data retrieval/cleaning, modeling, evaluation, and visualization.

Lead quantitative analysis and modeling of large real-world health datasets to estimate disease prevalence and incidence. Design statistical and machine learning models, build data pipelines and dashboards, ensure reproducibility and data quality, collaborate with public health experts, and prepare scientific reports and presentations.

Design and implement analytic methods to extract meaning from large, messy NSA/CSS datasets. Build and evaluate models, manage and curate data, translate mission needs into technical requirements, and communicate technical conclusions to non-technical stakeholders while accounting for collection and processing constraints.

Lead end-to-end delivery of strategic data science and AI solutions for AI infrastructure. Work with telemetry and experimentation at fleet scale to build, deploy, and measure predictive and prescriptive models. Partner with stakeholders, present insights, enable adoption, and promote responsible AI. Collaborate closely with data engineers and infrastructure teams to productionize scalable ML solutions.

Lead Data Scientist responsible for developing and implementing AI and ML solutions to support marketing and business engagement. The role involves technical leadership, model development, data analysis, stakeholder collaboration, and ensuring compliance with governance standards.

Lead product and program analytics for Microsoft skilling experiences. Design funnel and learner-journey analyses, define success metrics, build measurement frameworks, run experiments and causal analyses, develop competency/skill models, partner with product and engineering on telemetry, recommend content and curriculum improvements, mentor data scientists, and ensure data privacy and telemetry quality.