Top Remote Data Science Jobs

Lead the design, development, and operation of scalable data pipelines, trusted data products, metrics, and feature-ready datasets. Establish data quality, governance, orchestration, testing, observability, and engineering standards using Python, PySpark, SQL, dbt, Airflow, and Databricks. Partner with analytics, product, and engineering teams to support AI adoption and productivity measurement, while mentoring a team of two and driving delivery through ambiguous, cross-functional initiatives.

Build and operate reliable batch and streaming data pipelines, trusted data products, reusable metrics, and feature-ready datasets. Develop SQL/dbt transformations and production-grade Python/PySpark code, orchestrate workflows in Airflow, and implement data quality, governance, lineage, monitoring, and auditability controls. Partner with product, engineering, finance, and analytics stakeholders on experimentation and AI adoption measurement. Contribute to engineering standards, observability, documentation, incident response, code reviews, and peer mentoring.

Develop and maintain Java and React applications using Spring Boot, microservices, AWS ECS, and DevOps practices. Build scalable cloud applications, data pipelines, integrations, and tooling; optimize performance and reliability; troubleshoot production issues; and collaborate with designers, product owners, engineers, and business stakeholders. The role also involves data-driven insights, Agile delivery, CI/CD, code reviews, and exploring AI-enabled user interface solutions.

Designs, builds, and operationalizes production data science, machine learning, generative AI, and LLM systems. Responsibilities include data and AI pipelines, predictive models, RAG applications, experimentation, MLOps, model serving, monitoring, governance, cloud migration, and business analytics. The role partners with Product, Engineering, BI, and Data teams to translate business problems into scalable AI solutions and measurable product or operational improvements.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Develops statistical and machine learning models for kinematic behavior detection using live, historical, time-series, and geospatial data. Responsibilities include data integration, ETL, feature engineering, forecasting, anomaly detection, algorithm evaluation, simulation, visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to mission-focused software applications, APIs, data workflows, code reviews, troubleshooting, and agile development in a remote-first environment.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

Supports production data science capabilities by monitoring models, automations, pipelines, dashboards, and decision-support tools. Troubleshoots issues, evaluates performance, maintains documentation, and recommends improvements. Develops scalable data pipelines, workflows, analytical processes, dashboards, and AI-enabled solutions while conducting ad hoc analyses and supporting validation and deployment. Partners with senior data scientists and business stakeholders to operationalize research, communicate findings, and expand ownership across analytical portfolios.

Leads data science methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

Perform quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visuals, collaborate with cross-functional teams, use AI tools to scale analyses, and drive projects to completion to inform product decisions.

Performs exploratory data analysis, feature engineering, predictive modeling, statistical testing, and data visualization on large structured and unstructured datasets. Develops, validates, and improves predictive and prescriptive models, identifies business opportunities, and presents findings to stakeholders. Collaborates with machine learning engineers, product, and engineering teams on model deployment and internal data platforms. Establishes modeling best practices, supports data collection, and mentors associate data scientists.

Senior Data Scientist responsible for partnering with business leaders to frame ambiguous problems, deliver statistical and machine learning solutions, communicate insights, and drive measurable adoption. The role includes end-to-end analytical ownership, stakeholder influence, executive communication, Agile backlog management, cross-functional collaboration with engineering and technology teams, mentorship, governance, and improving analytics maturity.

Performs exploratory data analysis, feature engineering, predictive and prescriptive modeling, statistical testing, visualization, and business problem solving. Develops, validates, and deploys models in partnership with engineering and product teams, improves data sources and internal data platforms, communicates findings to stakeholders, and mentors associate data scientists.

Analyze large-scale healthcare claims data to improve pricing accuracy, benefit validation, adjudication, and operational performance. Develop analytical models, pipelines, testing frameworks, dashboards, and reporting assets. Validate claims logic across Facets and other adjudication platforms, investigate discrepancies, identify root causes, automate recurring processes, and present actionable recommendations to technical, business, operational, and executive stakeholders.

Lead AI/ML data science projects for payment integrity, from stakeholder requirements through production deployment. Develop, evaluate, monitor, and refine machine learning models that detect payment issues and prioritize clinical audit inventories. Collaborate with engineering, infrastructure, operations, and business teams; communicate methodologies and insights to technical and non-technical audiences; and ensure compliance with ethical, legal, regulatory, and organizational standards.

Leads Responsible AI data science initiatives by developing and validating AI testing protocols, automating analyses with Python, identifying PII and PHI vulnerabilities, and designing red-team scenarios for conversational AI. The role documents responsible AI practices, supports governance and regulatory compliance, communicates findings to legal, compliance, and technical stakeholders, and mentors colleagues. It requires expertise across traditional machine learning, generative AI, agentic techniques, AI ethics, fairness, and the full AI lifecycle.

The Senior Data Scientist supports Customer Success through deep-dive analysis, predictive modeling, causal inference, experimentation, and KPI development. The role analyzes churn, retention, renewals, upsell opportunities, and revenue trends; diagnoses metric shifts; builds data pipelines and dashboards; and develops AI/LLM-powered solutions. The scientist collaborates with stakeholders, communicates insights, improves data quality, and measures the impact of customer success initiatives and product changes.

Leads the strategy, architecture, development, deployment, monitoring, and improvement of applied AI, NLP, LLM, voice analytics, and automation solutions. Translates ambiguous business needs into scalable analytical architectures and production systems, establishes evaluation and governance standards, oversees data pipelines and model operations, troubleshoots complex issues, presents recommendations to stakeholders, shapes technical roadmaps, and mentors data scientists.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Own the company-wide revenue retention and add-on monetization analytics strategy. Build causal inference and retention models covering churn, MRR, GRR, NRR, attach, and usage. Partner with Finance, RevOps, Customer Success, CPaaS, AI, and product leaders to establish governed metrics, identify opportunities, guide experimentation, and influence product, pricing, and lifecycle decisions. Set analytical standards, develop reusable methods, and provide technical direction across teams.

Owns customer-growth analytics across onboarding, activation, trial-to-paid, multi-product adoption, retention, and expansion. The role develops segment-level behavioral and revenue insights, causal-inference and experimentation standards, canonical metrics, and value frameworks. It advises Growth, GTM, Finance, and product leaders, influences investment decisions, identifies data gaps, and sets technical direction for reusable growth-measurement methods across teams.

Lead commercial data science for Headway's commercial business: own roadmap, run proactive strategic analyses for executive accounts, monitor key metrics, enable self-service analytics, mentor data scientists, and translate technical findings into actionable recommendations tied to measurable outcomes.

Build and deploy NLP and machine learning models that analyze student and educator text. Develop classical ML and LLM-based features, production inference systems, validation and monitoring pipelines, data-drift detection, retraining workflows, and responsible AI guardrails. Partner with product, engineering, and data science teams to improve real-time education technology features while ensuring accuracy, fairness, privacy, and reliability.

Develops scalable data and analytics solutions for BILL’s compliance ecosystem. Responsibilities include extracting and analyzing data, building dashboards, maintaining compliance metrics, automating workflows, troubleshooting systems, performing root-cause analysis, and supporting technology accelerators. The role requires SQL, Python, Excel, business intelligence tools, and experience with risk or compliance systems in payments or financial services.

Leads responsible AI and model governance while building and deploying healthcare fraud, waste, and abuse models. Establishes standards for documentation, monitoring, bias, drift, validation, and production readiness. Evaluates generative AI use cases, develops policies, validates large-scale claims models, and presents methodology and controls to partners, auditors, and investigators. Works remotely with data scientists, BI developers, and healthcare subject-matter experts.