Top Remote Data Science Jobs

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Build reliability and quality models for consumer hardware, analyze manufacturing and telemetry data, develop predictive and decision-support systems, and translate findings into product, quality, firmware, warranty, and supply-chain decisions. The role combines statistical modeling, machine learning, experimentation, data engineering, and cross-functional technical leadership.

Leads development and deployment of production-grade machine learning models for customer lifetime value, lead scoring, personalization, recommendations, and revenue optimization. Builds scalable batch and real-time inference systems using Databricks and MLflow, establishes model governance and monitoring practices, designs business-impact experiments, and mentors data scientists. Partners with Data Engineering, Product, Marketing, and Analytics to deliver measurable improvements in acquisition, conversion, engagement, and profitability.

Own analytical relationships with travel technology partners, creating scalable reporting on supply, pricing, and performance. Conduct exploratory analysis, apply machine learning and statistical methods, build dashboards, automate recurring workflows with AI tools, and translate complex findings into recommendations for product, engineering, commercial, and executive audiences.

Leads data science research from question framing through modeling, validation, and recommendations. Partners with executives, business leaders, ML engineers, and data engineers to identify opportunities in risk, fraud, retention, pricing, and compliance. Builds forecasts and production-ready models, evaluates data as a strategic asset, and translates insights into product features, offerings, and measurable business outcomes.

Develops predictive models, machine learning solutions, data pipelines, statistical analyses, and internal data applications. The role uses Python and SQL Server to analyze structured and unstructured data, applying NLP, classification, regression, clustering, automation, and AI/LLM techniques. Responsibilities include maintaining data science platforms, supporting cross-functional decision-making, and communicating technical findings to non-technical audiences.

Unpaid remote internship focused on data extraction, scraping, mining, processing, cleaning, pipeline development, AI model training, and containerized microservices. The intern will use Django, Puppeteer, Selenium, Docker, Pandas, NumPy, Scikit-learn, Git, and GitHub while applying testing and data engineering practices. The role requires at least 30 hours weekly and may convert to full-time employment.

Build and deploy production machine learning systems for AIOps and IT operations. Develop novel AI/ML approaches, improve model reliability and accuracy, and work with large-scale operational and text data. Apply statistical modeling, anomaly detection, forecasting, clustering, NLP, causal inference, and root cause analysis. Collaborate with product and engineering teams, guide model and system design decisions, communicate findings, and mentor engineers while supporting production ML services.

Lead data science and analytics initiatives that optimize Coalition’s cyber insurance business. Responsibilities include experimentation, statistical analysis, funnel metric development, executive dashboards, data modeling, scalable analytical workflows, and data-backed product and revenue recommendations. The role partners with product, engineering, and insurance stakeholders, supports analytics agents, mentors junior team members, and establishes standards for code quality and documentation.

Leads development and deployment of statistical, machine learning, and causal inference models for customer decisioning. Designs experiments, estimates treatment effects and incremental value, evaluates targeting performance, and integrates analytical solutions into governed production systems. Partners with business and technology teams, communicates complex findings, and continuously improves models using experimental and operational results.

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.

Lead a data science team to design, build, and deploy large-scale, real-time AI/ML solutions (forecasting, diagnostics, LLM chatbot, retention models, optimization) to optimize GPU cloud gaming performance and user experience.

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.

Build, train, evaluate, and deploy deep learning models for credit modeling and loss prediction. Conduct representation learning experiments, integrate models into ML pipelines, and own projects from hypothesis through measurable business outcomes. Collaborate with data scientists, engineers, and product partners, communicate findings, and develop agentic tools that accelerate data science workflows.

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.

Build statistical and machine learning models using healthcare claims, financial, organizational, and public data. Develop healthcare metrics, clinical ontologies, entity-resolution models, affiliation networks, organizational maps, sourcing methodologies, and account-targeting signals. Partner with Product, Data Engineering, Engineering, and QA to move solutions from prototype to production. The role requires substantial healthcare data expertise, comfort with ambiguity, and the ability to communicate technical concepts clearly.

Build statistical and machine learning models using healthcare claims, financial, organizational, and public data. Develop entity resolution, clinical ontology, affiliation mapping, organizational inference, account segmentation, and data-sourcing methodologies. Partner with Product, Data Engineering, Engineering, and QA to move solutions from prototypes into production. Explain technical concepts clearly and work effectively amid ambiguity.

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.

Designs and deploys predictive models, segmentation frameworks, machine learning solutions, and data pipelines supporting marketing performance and customer analytics. Analyzes complex datasets, conducts experiments, validates and monitors models, and communicates insights to technical and non-technical stakeholders. Partners with strategy, media, client services, and engineering teams to create scalable data-driven solutions while contributing to peer review, knowledge sharing, and evolving data science practices.

Designs and deploys predictive models, segmentation frameworks, machine learning solutions, data pipelines, and experiments supporting marketing and customer analytics. 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.

Design and architect secure, scalable healthcare data systems; create core data models using TUVA, OMOP, and FHIR; build production data pipelines and EMR integrations; establish data quality and governance; optimize performance; deploy cloud infrastructure; and ensure HIPAA and HITRUST compliance. The role requires expert SQL and Python or Scala skills, advanced data warehouse and big-data experience, and at least eight years in data science or data engineering.