Top Remote Data Science Jobs

Analyze complex datasets to deliver actionable insights for production, resource allocation, and personnel decisions. Develop Tableau dashboards and visualizations, preprocess and clean structured and unstructured data using Python or R, and analyze time-series data. Collaborate with stakeholders, ensure data quality, document methodologies, and provide recommendations. The role requires an active TS/SCI with Polygraph clearance and is onsite in Northern Virginia.

Commercial Data Scientist will develop machine learning models, analyze large healthcare datasets, identify clinician behavioral patterns, and create reusable data products. The role partners with Product, Data, Strategy, Insights, and Sales teams to deliver client-facing analyses and data-driven recommendations. Responsibilities include statistical analysis, production model development, complex SQL querying, scalable data processing, automation, and communicating technical insights through compelling visualizations and narratives.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

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.

Serves as a client-facing data science and technology consultant for a healthcare data platform. Analyzes large-scale claims, clinical, and unstructured datasets; develops statistical, machine learning, predictive, and prescriptive models; and creates prototypes and scalable analytics solutions. Partners with product, engineering, and client services teams, translates client needs into recommendations, supports onboarding and troubleshooting, and provides technical guidance on AI, cloud analytics, and platform adoption.

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.

Leads the architecture, development, deployment, and governance of production machine learning and generative AI solutions. The role drives GenAI proof-of-concepts, establishes MLOps and responsible AI practices, mentors engineers, conducts technical reviews, and collaborates with cross-functional teams to deliver scalable AI capabilities. Expertise is required in statistical methods, deep learning, NLP or computer vision, cloud-native infrastructure, and production ML systems.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Build and support an advocate performance data platform using scalable pipelines, semantic models, governed metrics, and machine learning capabilities. Develop AI-ready data products, integrate operational and coaching data, establish data quality and lineage controls, and enable near-real-time performance insights. Deploy and maintain production ML solutions through MLOps, automation, monitoring, and CI/CD while collaborating with and mentoring technical teams.

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.

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Early-career Data Scientist supporting supply chain transformation through machine learning, forecasting, statistical modeling, data preparation, dashboards, and AI solutions. The role addresses inventory optimization, demand forecasting, production planning, delivery performance, and cost reduction. Responsibilities include developing analytical datasets and workflows with Python and SQL, evaluating models, integrating enterprise data, supporting cloud-based analytics, and communicating insights to business stakeholders in a manufacturing environment.

Analyze complex datasets, develop predictive models, identify trends, and deliver data-driven business insights. Collaborate with stakeholders to uncover growth opportunities, create visualizations, validate data quality, and apply machine learning, statistical modeling, and big data technologies. The role also requires staying current with emerging data science tools and industry trends.

Leads the full machine learning lifecycle for foundational personalization and recommendation models, including client and item representations, embeddings, retrieval, ranking, and generative approaches. Designs experiments, evaluates business impact, deploys and operates production models, and works with large-scale behavioral, transactional, textual, and visual data. The role also involves technical leadership, cross-functional collaboration, mentoring, and establishing modeling and engineering best practices.

Develop marketing effectiveness analytics using Marketing Mix Modeling, attribution, customer lifetime value, experimentation, and causal inference. Build automated, scalable model pipelines, insight-ready datasets, and self-serve dashboards. Analyze campaigns, support marketing strategy, communicate findings to executives, and lead high-impact projects independently while collaborating cross-functionally.

Own analytical strategy and develop AI/ML models for Zip’s financial products, including cash and rent advances. Lead ambiguous 0-to-1 initiatives using bank transaction data, establish evaluation frameworks, guide risk and growth decisions, and deliver models from experimentation through production. Provide technical direction to data scientists, mentor junior team members, collaborate with engineering, and advise senior stakeholders on model trade-offs. The role requires strong financial services, statistical, research, and machine learning expertise.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.

Leads commercial data science initiatives by transforming customer, market, and competitive data into actionable intelligence. Builds customer lifetime value, account health, and propensity-to-buy models; deploys machine learning, generative AI, LLM orchestration, and RAG solutions; and translates findings into dashboards and reports using Tableau, Power BI, or Looker. Supports global marketing, competitive intelligence, market research, and customer experience decisions.

Build and improve decision systems, predictive models, experiments, and advanced analytics solutions. Explore data sources, prototype technology-driven solutions, evaluate algorithms, and recommend strategic business changes through rigorous analysis. The role requires applying machine learning and software engineering practices to real-world product, customer, and financial problems.

The Data Scientist develops predictive models, segmentation frameworks, machine learning solutions, data pipelines, and experiments supporting marketing and customer analytics. The role analyzes complex datasets, validates and monitors models, communicates insights to technical and executive audiences, and partners with strategy, media, client services, and engineering teams to deliver scalable data-driven solutions.