Top Remote Data Science Jobs

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.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

Leads supply chain data science initiatives involving time series forecasting, demand planning, buying, inventory optimization, replenishment, and machine learning. Develops and productionizes advanced forecasting and optimization models, manages ML pipelines and deployment, documents methodologies, troubleshoots code, and collaborates with cross-functional stakeholders. Mentors data scientists, supports Agile processes, and provides technical leadership across model development, monitoring, retraining, and continuous improvement.

Own the scientific and product roadmap for monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead cross-functional delivery with Product, Engineering, Sales, and GTM; define measurement standards, evaluate trade-offs, support customer pilots, influence commercial decisions, and turn bespoke studies into scalable products. Remain hands-on in SQL, Python, statistical modeling, and causal inference while mentoring scientists and establishing reusable methods.

Own audience or measurement products for Fetch’s monetization business, translating advertiser goals and purchase behavior into predictive models, datasets, and causal studies. Build and validate audience expansion, measure advertising outcomes, assess bias and uncertainty, and improve products using campaign results. Partner with Product, Engineering, Sales, and GTM, present findings to customers, and develop reproducible analyses and production-ready assets.

Develop NLP, LLM, and machine learning pipelines to abstract clinical concepts from structured, unstructured, and multimodal healthcare data. Query healthcare source systems, curate and validate AI-ready datasets, support patient phenotyping and real-world evidence analyses, and deliver HIPAA-compliant data products. Collaborate with product and engineering teams, communicate findings to technical and clinical audiences, and improve data quality for customer AI applications. The role is remote with occasional quarterly travel.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

Build, evaluate, deploy, and monitor identity-resolution and record-matching models using large, noisy datasets. Design labeling and measurement strategies where no definitive answer key exists, use AI-assisted labeling, manage model retraining and lineage, review data quality, and communicate results and tradeoffs to product, engineering, and sales stakeholders. Mentor teammates and ensure responsible handling of personal data, opt-outs, retention, and lineage requirements.

Build and deploy production-ready data science models, monitor model performance and drift, develop data science metrics and features, and translate analytical findings into actionable guidance. The role partners with stakeholders to frame business questions, optimize data collection, create data assets, identify gaps, and deliver insights supporting pediatric healthcare strategy and patient care.

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.

Develop scalable data pipelines, statistical models, machine learning solutions, AI agents, dashboards, and decision-support tools for incentive compensation planning. Analyze large datasets, design quota and compensation models, evaluate business outcomes, ensure governance, and communicate recommendations to Finance, Engineering, product, sales, and executive stakeholders. The role also involves coding, debugging, automation, scenario modeling, and leading cross-functional analytical initiatives.

Lead end-to-end data science and machine learning projects, from scoping ambiguous client problems through production deployment. Build and evaluate statistical and ML models, analyze large datasets, run experiments, collaborate with engineers, create reusable tooling, communicate findings to diverse stakeholders, and mentor data scientists. The role combines hands-on data science with technical consulting and requires occasional customer on-call support.

Lead experimentation, causal inference, advanced analytics, machine learning, and Generative AI initiatives. Design and analyze marketing and product experiments, build scalable predictive and prescriptive models, establish statistical governance, develop data pipelines, influence executive stakeholders, and mentor data scientists. Translate complex business challenges into measurable analytical solutions across marketing, product, sales, risk, and compliance functions.

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.

Leads fraud detection, risk analysis, loss mitigation, and fraud strategy development. Builds analytical rules, predictive algorithms, monitoring dashboards, and automated controls using SQL, Python, Tableau, and AI tools. Partners with product, engineering, modeling, and operations teams to define requirements, validate strategies, optimize fraud KPIs, and manage deployments. The role also involves experimental design, governance, onboarding fraud typologies, and mentoring junior team members.

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.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Build and productionize machine learning, NLP, generative AI, and analytics solutions for healthcare and regulated federal data. Responsibilities include data preparation, feature development, experimentation, model evaluation, error analysis, validation, workflow monitoring, troubleshooting, and translating stakeholder requirements into technical solutions.

Build and operate AI-assisted data platforms using data preparation, knowledge graphs, semantic technologies, machine learning, and AI agents. Profile and reconcile enterprise data, create metadata and evaluation sets, assess language-model workflows, analyze human-review feedback, and improve platform performance. Partner with engineering teams to deploy validated solutions, maintain reproducible documentation, and communicate findings to technical and mission stakeholders. Handle sensitive data under governance and audit controls while supporting solution planning and proposals.

Develop and lead data science and machine learning initiatives, including exploratory analysis, predictive modeling, experimentation, recommendation systems, user classification, model monitoring, and production deployment. Partner with product, marketing, analytics, and engineering teams to convert user insights into automated services. Manage projects throughout their lifecycle, support strategic ML planning, and deliver solutions for personalization, cross-selling, and retargeting.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Build end-to-end data science, machine learning, forecasting, time-series, and AI solutions for supply chain and manufacturing operations. Develop analytical datasets, ETL/ELT workflows, dashboards, APIs, and production models using Python, SQL, cloud platforms, and modern data technologies. Partner with business and technical stakeholders to improve inventory, demand forecasting, delivery, production planning, cost, and operational efficiency. The role owns solutions from problem definition through deployment, monitoring, adoption, and continuous improvement.

Builds a talent network of experienced data scientists for potential future AI evaluation projects. Responsibilities may include creating grading criteria, evaluating AI-generated or human-produced data science work, writing evidence-based justifications, applying consistent judgment, and incorporating reviewer feedback. Candidates should have professional data science experience, strong Python and SQL skills, expertise in statistical modeling, machine learning, experimentation, causal inference, and excellent technical communication.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Develop statistical and machine learning models for clinical trial enrollment, patient matching, forecasting, and trial management. Monitor model performance and bias, build scalable data pipelines using AWS services and dbt, and ensure compliance with HIPAA, GDPR, and privacy standards. Collaborate with ML engineering, product, and clinical research stakeholders to integrate models into practical healthcare solutions.