Top Remote Data Science Jobs

Oversee development, validation, monitoring, and governance of consumer credit risk models used for loss forecasting, credit decisioning, regulatory reporting, and risk management. Apply statistical, econometric, and machine learning methods to PD, LGD, and EAD models; support CECL, stress testing, and macroeconomic scenario analysis. Assess model performance, data quality, and compliance while communicating findings to senior leaders, auditors, regulators, and other stakeholders.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

The Associate Data Scientist partners with clinical and operational teams to prepare and transform data, develop predictive models, apply statistical and AI techniques, and create actionable insights. Responsibilities include feature engineering, model optimization, data visualization, dashboards, stakeholder presentations, risk management, and Agile collaboration to improve patient care, hospital operations, and organizational outcomes.

Lead data science efforts to inform product and business strategy through quantitative analysis, experimentation, forecasting, and storytelling. Partner with cross-functional teams to define hypotheses, measure impact, influence roadmaps, present insights to senior leadership, and monitor key product metrics to drive value.

Leads advanced analytics, machine learning, statistical modeling, and NLP solution development using healthcare and operational data. Builds scalable pipelines, visualizations, and deployed models on Google Cloud Platform, translates findings for clinical and executive leaders, designs experiments, and establishes performance metrics. Requires collaboration across clinical, technical, and data engineering teams, with occasional on-site travel for executive presentations.

Designs AI risk stratification and evaluation methodologies for Veterans Health Administration systems, assessing performance, fairness, value, and post-deployment monitoring. Provides technical guidance on machine learning, healthcare data models, cohorts, synthetic data, and pilot evaluations. Develops risk management materials and supports governance decisions through quantitative analysis. Requires extensive applied data science experience, advanced education, and expertise in healthcare-focused machine learning.

Lead statistical modeling, AI/ML evaluation, data governance, data quality assurance, and executive analytics for VA EHR modernization. Build reproducible R-based analytics, reporting pipelines, dashboards, and governed data products using R, SQL, Python, Git, and cloud platforms. Evaluate model reliability, bias, drift, and operational value; integrate complex healthcare data; define metrics and validation rules; and advise stakeholders on auditable analytical practices.

Architect and develop voice-based chat, NLP, LLM, speech-processing, and natural-language search solutions for public safety applications. Build structured-data extraction, parsers, chatbots, REST APIs, cloud services, data governance, and optimized database queries. Develop multilingual, STT, and TTS datasets; fine-tune models; manage cloud provisioning; and mentor interns and junior data scientists.

Develops pricing and promotion optimization models using forecasting, deep learning, predictive modeling, and advanced analytics. Leads analytics solution development, interprets large datasets, communicates recommendations to technical and business audiences, and collaborates with cross-functional partners. The role also builds reusable algorithms, documents analytical solutions, and explores new data science techniques to improve profitability, efficiency, and customer experience.

Analyze, clean, and validate complex healthcare data; develop statistical, machine learning, and AI models; implement agentic workflows, RAG solutions, and model fine-tuning; deploy and monitor models in production; collaborate with technical and business teams; conduct research; and document and communicate findings.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Leads enterprise manufacturing AI, machine learning, and Industry 4.0 initiatives across multiple plants. Develops and deploys solutions for predictive maintenance, process and quality optimization, energy efficiency, throughput, and OEE improvement. Establishes scalable data and AI platforms, governance, and MLOps standards while integrating industrial data sources. Leads and mentors data science teams, prioritizes projects, allocates resources, partners with cross-functional manufacturing stakeholders, and presents strategic recommendations and business cases to executives.

Senior Data Scientist partnering with Product, Engineering, and Design teams to analyze user behavior, product performance, and marketplace dynamics. Responsibilities include developing metrics and reporting frameworks, designing experiments, diagnosing business trends, analyzing marketplace behavior, and aligning stakeholders on analytical priorities. The role uses SQL and Python alongside BigQuery, dbt Cloud, Airflow, Tableau, and Statsig, with a focus on product analytics, experimentation, and data-informed decision-making.

Develop machine learning and AI methods for cybersecurity products using large-scale network telemetry. Analyze anomalies and malicious behavior, engineer features, evaluate models on imbalanced and incomplete data, establish monitoring metrics, and conduct error analysis. Collaborate with threat researchers and software engineers to productionize solutions, research emerging techniques, communicate findings, document methodologies, and mentor data scientists.

Design and deploy deep learning systems for search ranking, personalized recommendations, geospatial search, and pickup-point selection. Translate business goals into ML objectives, lead offline and online evaluation, conduct A/B tests, and partner with engineering on production model serving and latency optimization. Own the production ML lifecycle, including monitoring, drift detection, retraining pipelines, and quality management across international markets.

Develop and operationalize machine learning and statistical models predicting weather-driven transmission and distribution outages. Integrate weather forecasts, infrastructure data, outage records, and geospatial layers into real-time forecasting pipelines. Apply spatio-temporal, probabilistic, and physics-based modeling to support utility decisions during extreme weather and wildfire events. Collaborate with meteorologists, risk modelers, software engineers, and utility customers while using agentic coding tools to accelerate development and validating scientific correctness.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

Conduct data science research on experimental methodologies and causal modeling, develop tools to analyze experimental data, prototype improvements to platform features, and communicate results to internal teams and external stakeholders to drive product and research decisions.

Conduct data science research advancing experimental and causal methodologies; develop robust tools and prototypes to analyze experimental data; improve core platform features; and communicate findings to internal teams and external stakeholders to drive insights and product improvements.

Designs and develops advanced analytics, machine learning, generative AI, agentic AI, and RAG solutions. The role includes framing business problems, curating and evaluating data, building analytical models, optimizing retrieval workflows, monitoring model performance, and translating stakeholder needs into measurable outcomes. The scientist also evaluates emerging technologies, establishes data science standards, communicates recommendations, and collaborates with business partners and other data scientists.

Design and deploy predictive forecasting and analytics to support Revenue operations. Build models for forecasting, churn, expansion, and pipeline analytics; create scalable dashboards; partner with Revenue Operations, Finance, and Data Engineering to improve data quality, KPI governance, and executive reporting to drive data-driven decisions and AI readiness.

Own end-to-end modeling for vendor matching, order routing, and supply-chain optimization. Build predictive, causal, RL and OR solutions; design autonomous decision logic; quantify counterfactual impact; ship production models with data engineering; mentor senior data scientists; write technical design docs; partner on quarterly priorities and expand into revenue cycle and finance domains.

Leads end-to-end machine learning model development, from business problem definition and data exploration through deployment, monitoring, documentation, and retraining. Mentors data scientists, collaborates with stakeholders and product teams, troubleshoots production code and ML pipelines, and guides Agile delivery. Requires advanced expertise in statistical modeling, machine learning, cloud platforms, MLOps, production coding, large datasets, containers, version control, and data lakes.

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.