Top Data Science Jobs

Administers UMM data-management infrastructure, including access controls, configuration, dashboards, data flows, ingestion validation, monitoring, and security compliance. Provides advanced tier-two technical support, troubleshoots issues, supports testing and user acceptance, documents configurations, and coordinates enhancements with engineering and data stakeholders. The role supports distributed DoD and Intelligence Community users and requires on-site work in a DIA SCIF with an active TS/SCI CI Poly clearance.

Develops standardized performance methodologies and analytical products using intelligence and operational data. The role identifies trends, anomalies, risks, and optimization opportunities; creates KPIs, scorecards, dashboards, reports, and executive briefings; and supports resource allocation, operational planning, collection management, and leadership decisions. Responsibilities include data validation, cleansing recommendations, documentation of analytical methods and limitations, and coordination with operational stakeholders, engineers, administrators, and leadership.

Develops and evaluates AI and machine-learning models for intelligence collection prioritization, tasking automation, resource optimization, and mission planning. Responsibilities include data preparation, predictive modeling, validation, monitoring, explainability, responsible AI, and collaboration with software engineers on deployment. The role briefs technical and mission stakeholders and requires work in a DIA SCIF supporting secure DoD and Intelligence Community operations.

Own data science for payment performance, optimization, timing, retry strategies, monitoring, experimentation, and fraud detection. Build payment-health scorecards, anomaly monitoring, payment-risk models, and production ML systems using Python, SQL, PySpark, Databricks, and MLOps tooling. Partner with Engineering, Product, and Risk to shape payment strategy and roadmap while applying causal inference, feature engineering, and production model monitoring.

Build Boulevard’s Product Intelligence function by developing product data foundations, pipelines, dashboards, experimentation practices, and customer behavior models. Partner with Product, Engineering, and leadership to deliver actionable insights, self-serve analytics, and productionized models. Establish scalable processes, instrumentation standards, statistical workflows, and data-driven decision-making across the organization.

Leads operations research and data science initiatives across supply chain, procurement, and engineering. Develops optimization, forecasting, statistical, and machine learning models for inventory, scheduling, capacity planning, demand, cost, and vendor analytics. Establishes modeling, validation, backtesting, monitoring, and retraining standards; partners with business leaders to drive adoption; and mentors data scientists and AI engineers.

Own Yoodli’s data function and measurement strategy, including experimentation, LLM evaluation, analytics infrastructure, dashboards, and predictive models. Partner with product, engineering, executives, and customers to turn analysis into product and commercial decisions. Establish data standards, communicate recommendations, mentor teams, and potentially build the data science organization over time. The role requires hybrid work in the Greater Seattle Area three days per week.

Develops data pipelines, curates and analyzes large datasets, builds quantitative models, validates data and results, and deploys analytics solutions with product teams. Responsibilities include discovering useful datasets, ensuring data quality and reliability, supporting customer-driven requirements, and integrating solutions into interconnected systems. The role requires strong Python and data-handling skills, database expertise, large-scale batch processing experience, and U.S. citizenship.

Develop and productionize statistical and machine learning solutions for financial crime detection. Responsibilities include NLP, entity resolution, document classification, knowledge graphs, topic modeling, scalable ML pipelines, large-scale data analysis, and translating business challenges into quantitative problems. The role collaborates with Product, Platform Engineering, Sales, and clients, requiring clear communication with technical and non-technical audiences.

Develop statistical and machine learning models using soccer tracking and event data to support player recruitment decisions. Build spatio-temporal models, metrics, analyses, and interpretation tools; validate models; communicate findings to professional club clients; and partner with engineering on cloud deployment and production reliability. The role also includes code review, model maintenance, and advancing analytical methods.

Lead data science and marketing analytics initiatives that improve member activation, engagement, retention, and health outcomes. Develop segmentation, propensity, predictive, and prescriptive models; design experiments and A/B tests; establish measurement frameworks; and translate insights into campaign and product strategies. Partner with Product, Marketing, Care Management, Data Engineering, and MLOps teams to operationalize targeting models and scale personalized engagement programs.

Supports intelligence community projects through data acquisition, cleaning, statistical analysis, AI/ML model testing and evaluation, dashboards, visualizations, and reproducible documentation. Collaborates with senior staff and stakeholders to produce analytical outputs addressing defined business questions. The role requires an active TS/SCI clearance with CI polygraph and supports Department of Defense or Intelligence Community clients.

Supports intelligence community programs through data acquisition, preparation, statistical analysis, analytics, and testing and evaluation of AI/ML models. Builds reports, dashboards, and visualizations; documents reproducible methods and results; and presents findings to stakeholders. The role requires collaboration with federal defense and intelligence clients and an active TS/SCI clearance with CI polygraph.

Develops NLP and machine learning solutions for VA disability claims, extracting and structuring medical and claims data. Builds and maintains NLP pipelines, tunes transformer models, validates accuracy, documents workflows, collaborates with VA stakeholders, and integrates models into government systems. Requires healthcare or claims data experience, cloud familiarity, and the ability to communicate technical concepts effectively.

Develop data science solutions for Navy mission applications by analyzing complex structured and unstructured data. Build algorithms, scripts, predictive analytics, statistical and simulation models, and machine learning solutions. Apply data engineering practices, statistical software, and natural language processing techniques to generate actionable insights that support military decision-making. Collaborate with customers to understand analytical needs and communicate findings.

Conduct quantitative analysis, process complex and geospatial data, develop visualizations, and create data-driven insights. Build Python-based data-cleaning and manipulation workflows while working with AI/ML technologies and multiple file formats. Desired work includes NLP document extraction, computer vision, automated collection and reporting, data standardization, and statistical techniques such as classification, regression, clustering, data reduction, and causal modeling.

Leads enterprise data science, AI, machine learning, and advanced analytics strategy supporting NASIC. Designs analytical architectures, optimizes complex data workflows, develops governance frameworks, evaluates cloud capabilities, and guides AI/ML deployment. Produces executive dashboards and progress reports, creates technical policies, and coordinates with data, AI, infrastructure, and intelligence stakeholders. The role requires extensive computational experience, DoD/IC standards expertise, cloud proficiency, and an active TS/SCI clearance.

Develops and deploys AI/ML and intelligent automation workflows for complex intelligence datasets. Builds statistical models, data pipelines, dashboards, and scalable software outputs within secure cloud infrastructure. Integrates data strategies with Zero Trust Architecture, supports reporting and technology prototyping, and collaborates with engineers, database administrators, and product owners. Requires active TS/SCI clearance and onsite support at Wright-Patterson AFB.

Develops automated collection models, analytic models, workflow automations, and data-processing solutions supporting intelligence strategies. Evaluates and improves automated solutions using performance data and stakeholder feedback. Applies data science, visual programming, geospatial tools, statistical methods, machine learning, and visualization to address intelligence integration, automated collection, information sharing, and analytic workflow challenges.

Develop and maintain operational data pipelines supporting multiple data formats and interfaces. Query systems and migrate data using APIs, work with SQL or NoSQL databases and cloud architectures, and use Python, JavaScript, or Java. Monitor data pipelines, communicate complex technical concepts, and potentially support NGA enterprise solutions, MLOps, ATO processes, SAFe implementation, and dynamic visualizations.

Builds and maintains data structures, ETL scripts, dashboards, data visualizations, and automation routines. Performs data cleaning, validation, metadata governance, and basic AI/ML algorithm execution. Supports Agile sprint activities, pipeline documentation, database maintenance, and troubleshooting of ingestion and dashboard issues. Requires onsite work at Wright-Patterson AFB supporting NASIC.

Build and deploy data science models for CPG brands, focusing on demand and sales forecasting, promotion effectiveness, pricing, elasticity, and cannibalization. Analyze messy retail, inventory, trade promotion, and supply-chain data; develop statistical validation and evaluation methods; and communicate model insights and uncertainty to technical and non-technical stakeholders. Collaborate with AI/ML engineers to move analyses into production.

Drive product analytics across feature adoption, usage, pricing, customer health, and experimentation. Partner with product, engineering, and business leaders to identify opportunities, deliver actionable insights, improve self-serve analytics and governance, and strengthen data pipelines and analysis quality. The role requires communicating complex findings clearly and working from Aircall’s San Francisco office two days per week.

Architects and deploys enterprise-scale statistical, predictive, and machine learning solutions across healthcare, finance, operations, and member domains. Owns the full analytical lifecycle, including feature engineering, validation, monitoring, retraining, explainability, governance, and retirement. Partners with executives, engineering leaders, and platform owners to deliver production-ready analytics within Microsoft’s cloud ecosystem. Establishes standards for model risk management, auditability, reproducibility, and experimentation while guiding other data professionals and communicating insights to senior leadership.

Designs and maintains large-scale APIs and systems supporting money movement, translates product requirements into technical specifications, and guides product strategy. The role requires data science or quantitative modeling expertise, production model development, experimentation, SQL, Python or R, and translating analysis into business outcomes. Responsibilities also include reviewing API launches, ensuring system compatibility and extensibility, representing the team in technical discussions, mentoring engineers, and establishing engineering principles.