Top Remote Data Science Jobs

The Data Scientist will develop tools for enhancing signature management capabilities, integrate diverse data holdings, and leverage AI/ML solutions for operational support.

Customer-facing data scientist advising customers on experiment design, statistical methodology, and experimentation architecture; running and analyzing A/B tests; building scalable experimentation programs; creating repeatable frameworks and technical enablement; partnering with Sales, Solutions Engineering, Product, and Engineering to drive customer success.

Remote multi-role data position covering Data Analyst, Data Engineer, and Data Scientist duties: analyze and visualize data, write complex SQL, build dashboards, perform A/B testing and segmentation; design scalable ETL/ELT pipelines, maintain data lakes/warehouses, enforce data quality and monitoring; and develop machine learning models and predictive analytics to drive business decisions while collaborating across teams.

The Data Scientist will build predictive models, drive forecasting, design experiments, and translate findings for stakeholders, optimizing healthcare operations.

Perform statistical analysis, predictive modeling, and data visualization on workforce and training data. Build dashboards, produce ad hoc analyses and decision-support products, advise on data governance/privacy, and collaborate with stakeholders to define metrics, deliverables, and reproducible analytic workflows.

Use statistical and causal inference methods to build, implement, and monitor marketing models (Bayesian MMM, multi-touch attribution), analyze A/B and uplift tests, partner with marketing to test and drive data-informed decisions from large web-product datasets.

Lead and grow a 4–6 person data science team, set the roadmap, deliver marketplace forecasting and production ML (matching, scoring), embed data science cross-functionally, and establish modeling, experimentation, and monitoring best practices.

The Data Scientist II will design, build, and deploy machine learning models and data products, translating business problems into data-driven solutions, while collaborating with various stakeholders and ensuring reliable, production-ready outcomes.

Contract data scientist to remotely gather, extract, and analyze technical information about AI models, benchmarks, data centers, and companies. Tasks include reading ML papers, tracking leaderboards, processing satellite images/permits, compiling infrastructure details, and producing data insights for research and policy. Part-time (10–30 hrs/week), flexible schedule with occasional Pacific Time meetings.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

Build, validate, and productionize clustering and attribution heuristics on large multi-chain on‑chain datasets. Own end-to-end data pipelines, investigate edge cases (mixers, bridges, consolidation), benchmark against ground truth, and partner with investigations and product to define and defend precision/coverage metrics.

As a Senior Data Scientist, you'll optimize search relevance, retrieve content, analyze data, and collaborate with teams to enhance product strategies.

The Data Scientist will analyze geospatial data, communicate insights, collaborate with teams, and enhance data processes to support national security missions.

Build, maintain, and improve decision systems and predictive models; explore internal and external data; design and run in-market experiments; prototype solutions; recommend data-driven strategic changes; implement advanced analytics methods.

The role involves deploying data science solutions in cybersecurity, collaborating with customers and teams to operationalize models and enhance security insights.

Lead data-driven strategy by partnering with senior executives to deliver rigorous analytical solutions. Design and implement sophisticated analyses on large-scale data, build trusted cross-functional relationships, mentor data scientists, and influence long-term product and business decisions.

Design, validate, and deploy scalable statistical and distributional models for enterprise consumer analytics. Implement rigorous methodologies, convert ad-hoc analyses into version-controlled analytical libraries, and partner with Data Engineering to optimize performance and scale compute workloads (including cloud/Databricks). Deliver reproducible analytics powering dashboards and LLM-driven applications, with a focus on panel and transaction-level consumer data.

Lead end-to-end data science initiatives across pricing, dispatch, ETA, routing, and forecasting. Turn ambiguous logistics problems into production ML solutions, build data pipelines, partner with engineering to deploy models, establish evaluation and A/B testing, and work directly with customers and leadership to measure business impact.

Lead end-to-end data science efforts across pricing, dispatch, carrier selection, ETA prediction, routing, and forecasting. Own discovery through production, build models and pipelines with Python/SQL/Snowflake, partner with engineering to serve models, establish evaluation/monitoring/A-B testing, and measure impact on cost, reliability, and delivery performance while working closely with product, operations, and customers.

Build statistical and predictive models to measure and optimize marketing, sales, and customer success impact on pipeline, bookings, ARR, retention, and expansion. Analyze paid media performance and conversion funnels, design experiments and causal measurement, forecast revenue, partner with cross-functional teams to define metrics, and communicate data-driven recommendations to senior leadership while improving measurement methodologies and data quality.

Design and build cloud-based analytical products across the Pharmacy Benefits Management ecosystem. Develop predictive and prescriptive models, perform feature engineering and EDA, collaborate with product, data engineering, and business teams, and modernize legacy systems to support forecasting, pricing, and decision-making.

Lead a 5–8 person Data Science team to develop and operationalize FWA detection models using SAS and Python on multi‑payer healthcare claims. Oversee model development, data pipelines, quality reviews, stakeholder presentations, and collaboration with SMEs and BI developers. Split role ~40% management, 60% hands-on coding/analysis.

Design, build, and deploy predictive GTM models (LTV, attribution, propensity). Lead experimentation and causal analyses, define KPI measurement frameworks, partner with Data Engineering, and translate statistical insights into actionable recommendations for Sales, Marketing, and Customer Success.

Lead the data science function for a freight pricing venture: develop and deploy ML models for dynamic pricing, demand forecasting, elasticity, and load acceptance; design pricing experiments; partner with engineering to productionize and monitor models; validate business assumptions and set model governance; translate insights to product, commercial, and executive stakeholders.

Senior Data Scientist for Corporate Engineering applying statistics and machine learning to telematics and vehicle service data. Responsibilities include data acquisition and processing (HDFS/Kafka), model development for diagnostics/prognostics and preventive maintenance, cloud analytics research, cross-functional collaboration, validation/verification, visualization, and supporting proposals and product/service improvements.