Top Remote Data Science Jobs

Design, build, and deploy predictive models for risk and fraud using R, Python, SAS, and SQL. Analyze large datasets, develop cloud-based analytic solutions, validate scoring code with stakeholders, and document statistical methodologies and best practices.

Support ARNG CFO analytics by preparing and facilitating PPBC-G forums, coordinating across staffs, designing and executing data analyses and ML models, and developing/enhancing websites, applications, and secure database access for program portfolio analytics and executive presentations.

As the first Data Scientist, you will build models and algorithms from scratch, impacting the company's success in optimizing insurance portfolios.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Perform data cleansing, ETL, and preprocessing; analyze Retail CPG sales, inventory, and customer data; build predictive models for forecasting and segmentation; research and deploy Agentic frameworks and intelligent agents; produce visualizations and communicate insights to cross-functional stakeholders to optimize supply chain, marketing, and customer experience.

Lead experimentation and causal inference at company-wide scale: define methodology, own statistical approaches for small samples and clustered data, apply causal methods when experiments arent feasible, run experiment review, build tooling and curriculum, and partner with analytics and AI/ML teams to ensure decision-grade results.

Design and run exploratory analyses, build Python-based data pipelines, apply statistical modeling, graph algorithms and ML to multi-INT datasets, and develop interactive dashboards (Power BI/Tableau/Grafana). Mentor junior staff, brief senior leadership, and produce technical reports and documentation in a DoD/IC environment.

Owner of analytics for EHR, revenue cycle, and operations: design and run experiments, analyze financial and operational metrics, investigate billing and account issues, and deliver actionable insights to Product, Operations, Finance, and Customer Experience to improve accuracy, reduce friction, and support growth.

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.

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.

Build models, heuristics, and analytics that optimize model/provider routing and user experience for a large LLM marketplace. Partner with product and engineering to design user-facing data features, define product metrics, run deep analyses, and ship data-driven improvements tied to roadmap decisions. Scale analytical foundation in ClickHouse/dbt.

Design measurement frameworks and advanced causal inference solutions for marketplace allocation and recommendation systems. Partner with Product, Engineering, and business stakeholders to translate needs into scalable data science tools, run experiments, quantify impact, and drive implementation for Netflix discovery surfaces.

Support IRS projects by developing, validating, and deploying predictive and statistical models on large tax and financial datasets. Perform data cleaning, feature engineering, anomaly detection, and risk scoring using Python/R, SQL, and Databricks. Communicate results to stakeholders and collaborate with cross-functional teams to inform audit prioritization, fraud detection, and compliance efforts.

Design, develop, validate, and deploy statistical and machine learning models to detect fraud, anomalies, and noncompliance in large tax and financial datasets. Clean and feature-engineer data, run robustness and sensitivity tests, collaborate with stakeholders, and present technical and non-technical findings. Support end-to-end data science lifecycle and productionization in a government environment.

Lead and support full-lifecycle data science projects (forecasting, NLP, classification/regression, optimization). Design production-ready solutions within client environments, consult with stakeholders to define ambiguous problems, communicate technical findings, and develop/teach bespoke analytics training.

Support IRS tax compliance and fraud detection by developing, validating, and deploying predictive and statistical models. Clean and analyze large datasets, apply supervised and unsupervised learning (including NLP and graph analytics), collaborate with clients and teams, and deliver technical briefings and defensible, explainable model results.

Work with clients and technical staff to design, develop, and deploy predictive models and data-driven analytical solutions. Perform data extraction, ingestion, and modeling; evaluate and improve models; translate business rules into technical specifications and communicate results to stakeholders.

Support IRS analytics to detect fraud and non-compliance by developing, validating, and deploying predictive and statistical models. Clean and analyze large datasets, perform feature engineering, apply supervised and unsupervised techniques (including NLP and graph analytics), collaborate with stakeholders, and deliver technical and non-technical briefings. Work across the full data science lifecycle and operate in secure government environments.

Develop, validate, and deploy predictive and statistical models using structured and unstructured tax and financial data to detect fraud and non-compliance. Clean and wrangle large datasets, perform feature engineering, apply supervised and unsupervised techniques, and communicate results to technical and non-technical stakeholders. Collaborate with clients and cross-functional teams and support model deployment and documentation in secure government environments.

Lead and build Neural Earth's geospatial data science function: design and deploy Python-based geospatial models, architect production geospatial data pipelines, validate reproducible methods, translate scientific outputs into customer-ready products, and manage and mentor at least one direct report while advising customers and stakeholders.

Internship role to develop and deploy user-growth algorithms: user segmentation, recommendations, LTV/churn/conversion models, A/B testing, causal inference analyses, and collaborate with product and engineering to drive measurable growth.

Analyze large marketing datasets, build predictive models for segmentation, pricing, and demand forecasting, support targeted campaigns and retention, partner with product and marketing, apply statistical and ML methods, ensure data quality with engineering, and communicate insights to stakeholders to drive conversion and retention improvements.

Lead end-to-end data science projects: define problems, build/test hypotheses, deliver scalable solutions for productization. Perform advanced statistical and time-series analyses, collaborate cross-functionally, and translate insights into customer value while supporting deployment and system optimization.