Top Data Science Jobs

Develop and execute analyses, predictive models, and reporting to improve Medicare Star Ratings. Analyze large healthcare datasets, extract actionable insights, partner with clinical/operations/product/IT teams, and present recommendations to stakeholders.

Work cross-functionally in 6–8 week analytics sprints to build models and deliver actionable recommendations for profit optimization, demand estimation, price-sensitivity and counterfactual analyses. Communicate insights to leadership, prepare presentations, and integrate advanced Yield Management analytics into business decisions.

Perform data extraction (including VPN data), integrate into databases, and analyze datasets using statistical and computational methods. Apply knowledge of DX, XKS, and CASNs to guide data selection, build analytic models, and produce actionable insights. Translate mission needs into technical requirements and communicate results to non-technical stakeholders while accounting for government data constraints.

Lead client-facing data science efforts to develop AI/ML solutions (including LLMs/agentic AI), guide teammates, clean and model complex data, evaluate model accuracy, visualize results for leadership, and work on classified networks requiring TS/SCI clearance.

Design, refine, and operationalize geospatial analytic models to bring consistency and transparency to GEOINT analysis. Extract, transform, and structure complex datasets, automate workflows, build dashboards, integrate models with analytic platforms, and ensure tradecraft integrity and defensible scoring frameworks that withstand scrutiny.

Collect, validate, and analyze data from multiple sources to ensure software data integrity. Design tools, data models, and algorithms for validation and accuracy. Produce reports with findings and recommendations.

Design, refine, and operationalize GEOINT analytic models to convert inconsistent geospatial attributes into defensible scoring frameworks. Automate workflows, build dashboards, integrate models with analytic platforms, and collaborate with analysts and leadership to ensure methodological rigor and transparency.

Lead design and delivery of people analytics products using predictive models and language models. Build unified workforce data models, semantic layers, governance, continuous listening & survey frameworks, and enforce experimental and psychometric rigor to inform talent, retention, and team-performance decisions.

Support Defense Intelligence analysts by building data pipelines, ML models, and analytics for multi‑INT data. Perform data engineering, mining, predictive analysis, and deliver visualizations. Use Python, R, SQL, Power BI, and Tableau. Work requires active TS/SCI clearance and 8+ years of data science experience.

Implement and maintain geospatial data science and remote sensing workflows: acquire, clean, and process raster/vector and Earth-observation data; run and validate GeoAI and ML/DL models; conduct accuracy assessments and documentation; automate repeatable processing; collaborate with technical staff and translate results for partners and clients.

Lead design and oversight of end-to-end geospatial data science and remote sensing workflows; translate scientific questions into reproducible methods; validate and document analyses; mentor technical staff; guide model development, quality control, and delivery for environmental and conservation applications.

Lead data science projects to identify growth and efficiency opportunities, design and deploy ML/DL models, define data acquisition strategies, mentor junior data scientists, manage stakeholders, and help build AI/ML capabilities for Honeywell Forge on cloud and distributed compute platforms.

Lead and execute advanced data science projects: identify opportunities, define data acquisition, build and deploy ML/DL models on cloud and containerized pipelines, mentor junior data scientists, manage stakeholders, and help shape enterprise AI/ML strategy for Honeywell Forge.

Lead predictive modeling and statistical analyses to support member and provider interventions, evaluate care management programs, develop/customize algorithms, process large distributed datasets (e.g., Denodo), score models in batch/real-time, visualize results, and present findings to stakeholders and clients.

Lead end-to-end experimentation and analytics for the fintech product: design A/B and multivariate tests, measure impacts on behavior, unit economics, and risk; turn model outputs into business rules; define metrics and dashboards; partner with product, finance, risk, and engineering; and communicate results to non-technical stakeholders.

Partner with product teams to define measurable outcomes, build and govern metrics, design and evaluate experiments, validate data quality, create dashboards and data models, and translate analyses into actionable product recommendations while improving analytics instrumentation and scalable practices.

Partner with product teams to define and govern success metrics, design and evaluate experiments, build dashboards and data models, validate and prepare analysis-ready data, and translate analyses into actionable recommendations to guide product decisions and scale analytics practices.

Perform exploratory data analysis, feature engineering, and develop predictive/machine learning models on large structured and unstructured datasets. Validate, test, and compare models, create visualizations and presentations for stakeholders, drive data collection and platform improvements, partner on model deployment, and mentor associate data scientists to deliver actionable business insights.

Lead development, maintenance, and monitoring of credit risk models for lending portfolios. Use SQL and Python to build pipelines, perform EDA, forecast losses, automate dashboards, produce audit-ready documentation, support model governance/validation, and communicate insights to stakeholders.

The Data Scientist/ML Engineer designs and deploys Machine Learning algorithms, assists federal customers, and contributes to new feature design.

The Data Scientist will build models using statistical and machine learning techniques, conduct analyses, collaborate with teams, and improve model performance.

Lead end-to-end data science initiatives, developing AI solutions for personalization, marketing, and product experience while collaborating with cross-functional teams on machine learning models.

Lead measurement and experimentation for the Identity product, define metrics and frameworks, design A/B tests and causal analyses, build datasets and dashboards, quantify growth vs. abuse trade-offs, and partner cross-functionally to inform product strategy.

Design, implement, and maintain marketing inference models (e.g., MMM and attribution). Analyze large datasets and A/B/uplift experiments with rigorous statistics, build predictive and causal models, partner with marketing to generate testable ideas, and communicate insights to drive marketing decisions.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.