Top Remote Data Science Jobs in Washington, DC

The Data Scientist will analyze user behavior, create segmentation strategies, design experiments, and track business metrics to drive growth in a SaaS company.

Lead product analytics for in-vehicle experiences: define KPIs and instrumentation, perform deep-dive behavioral analyses, build dashboards and self-serve analytics, guide experimentation and causal inference, and mentor teams to scale analytics practices across the Vehicle Product organization.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Lead end-to-end data science projects, develop machine learning models, collaborate with stakeholders, mentor team members, and communicate insights effectively.

Design and train LLMs and agentic systems for cybersecurity using RLHF/RLAIF and supervised fine-tuning. Build agent architectures, tooling, retrieval/memory, and evaluation pipelines with rigorous statistical benchmarking. Optimize prompts and inference, collaborate with engineers to productionize prototypes, and drive research on agent planning, safety, and reliability.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

Data Scientists at BlueLabs build predictive models, collaborate on analytics products, and tackle complex data challenges while ensuring high data quality.

Lead data-science and risk-analytics for risk-based review selection. Build, maintain, and operate models, scripts, formulas, and dashboards that rank partner agencies by risk. Aggregate and validate data, compute health scores, visualize findings and trends, support quarterly risk meetings, and maintain the Risk Support Work Plan.

Perform quantitative real estate and urban economic analysis, applying econometric and spatial methods to synthesize property, macroeconomic, and geospatial data. Build and maintain automated data pipelines, ensure data integrity, document dataflows, and prototype ML/AI methods. Collaborate with research, GIS, and engineering teams to produce reproducible insights and analytical products for stakeholders.

Develop and operationalize graph-based analytics for fraud detection and investigative analysis. Build graph models, apply graph algorithms and ML to detect anomalies, support entity resolution, create visualizations and dashboards, and collaborate with investigators and stakeholders to deploy and maintain production solutions.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

The Senior Data Scientist will build frameworks for data-driven decision making, develop predictive models, ensure data quality, and enhance customer experiences through analytics and operational insights.

Lead analytical efforts in Real World Data research, develop specifications and methodologies, manage projects, and communicate findings to stakeholders.

Develop, deploy, and operationalize analytical and ML/AI models for cybersecurity use cases. Collaborate with product and engineering teams to enable and migrate production workflows, work with databases and data architectures, create visualizations and reports, adopt new cloud orchestration and data virtualization technologies, and mentor junior cyber data scientists.

The Sr. Football Data Scientist will build, deploy, and refine analytical models for football insights, managing large datasets and collaborating with teams for high-quality analytics solutions.

As a Staff Data Scientist, you'll develop and deploy machine learning models to enhance renter experiences and optimize property partnerships, collaborating across teams to translate data insights into actionable strategies.

As a Data Scientist III, you'll build customer identity systems, develop recommendation models, predictive analytics for customer behavior, and collaborate with ML Ops and Data Engineering.

The Data Scientist will enhance statistical models for forecasting, analyze datasets for operational planning, and communicate insights to stakeholders.

This role involves partnering with sales to design data-driven solutions, engaging with customers, and supporting pre-sales engagements.

The Data Scientist designs and builds data structures for AI applications, manages vector and feature stores, leads data discovery, and collaborates on responsible data use while ensuring high-quality, governed datasets for ML models and applications.

This role involves leading data science projects, designing technical solutions, implementing machine learning methodologies, and developing data workflows for cybersecurity applications.

The Data Scientist II will develop machine learning algorithms, architect data solutions, and support cybersecurity initiatives by analyzing complex data sets.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Develop and refine predictive models, perform exploratory data analysis, and generate AI-driven insights for DoD missions. Integrate multi-source data, apply geospatial analytics, iterate models via Agile sprints, produce performance and cost reports, and optimize analytic workflows for operational deployment.