Top Remote Data Science Jobs

Lead data science and AI consulting engagements for technology, media, and entertainment clients. Develop Agentic AI solutions, statistical models, experiments, KPIs, and advanced analytics to solve business problems. Manage client relationships, teams, staffing, quality assurance, stakeholder communication, and executive presentations. Support proposals, business development, thought leadership, training, and growth of the analytics practice.

The Senior Data Scientist will conduct product analysis, build advanced models and analytics tools, evaluate A/B tests, develop automated dashboards, and generate recommendations for product strategy. The role partners with business stakeholders and Engineering to improve data infrastructure and governance, communicate statistical insights, and support data-driven decisions across Product, Marketing, and Finance.

Own product-led growth analytics across activation, engagement, conversion, monetization, and retention. Lead experimentation, statistical analysis, causal inference, metric development, and proactive opportunity discovery. Build scalable analytical systems, monitoring frameworks, semantic models, and AI-powered tools. Partner with Product, Engineering, and leadership to shape product bets, investment decisions, and roadmap priorities. Work autonomously in a remote-first environment and communicate complex findings through clear, actionable recommendations.

Develop and own advanced machine learning and AI models using bank transaction and cashflow data to enable financial product capabilities, manage risk, and drive profitable growth. Design experiments, build proofs of concept, monitor production models, and collaborate with data and machine learning engineering teams on scalability. Communicate findings and tradeoffs to business stakeholders while applying statistical, research, and financial services expertise.

Build predictive and machine learning models for patient needs, operational forecasting, churn, advocate quality, and risk. Design A/B tests and causal inference studies, apply advanced statistical methods, productionize models with data engineers, and communicate insights to business stakeholders. The role requires Python, advanced SQL, forecasting expertise, statistical proficiency, data privacy awareness, and the ability to work independently in a fast-paced healthcare startup.

Lead computational platform development for biological design, optimization, and functional prediction. Collaborate with wet-lab scientists to validate predictions, oversee data infrastructure and computational workflows, and contribute to technology roadmaps, funding proposals, and partnerships. The role requires developing machine learning methods for biological sequence and structure analysis, translating biological problems into computational solutions, and leading technical teams across computational and experimental disciplines.

Build and deploy machine learning models that personalize insurance products, analyze product data, design experiments, and measure model impact. The role owns the full data science lifecycle, including data wrangling, production coding, model monitoring, retraining, and experimentation. Responsibilities also include cross-functional collaboration, code reviews, mentoring data scientists, and improving platform practices.

Build and evaluate machine learning models and features for fraud detection across account opening, account takeover, and identity risk. Investigate fraud patterns, analyze complex datasets, develop production-ready code, validate models, and support monitoring and deployment. Collaborate with senior data scientists, engineers, and score monitoring teams while communicating findings to technical and nontechnical audiences. Apply data privacy, explainability, documentation, validation, and governance standards.

Senior Data Scientist responsible for defining product metrics, designing and evaluating experiments, analyzing growth, retention, and monetization, and building trusted datasets, dbt models, and dashboards. Partners with Product, Engineering, Design, and Marketing to guide roadmap decisions, establish analytical standards, communicate insights to leadership, and mentor teammates.

Leads data science for performance marketing and user growth. Develops marketing measurement, experimentation, causal inference, forecasting, propensity, lifetime value, budgeting, and retention models. Builds dashboards, reporting tools, ETL pipelines, and experimentation frameworks, while defining success metrics and translating complex analyses into strategic recommendations. Partners with Marketing, Product, Engineering, Analytics, and senior leadership to optimize paid marketing investments and business outcomes.

Lead customer-facing data science engagements using production CRM, marketing, firmographic, and intent data. Build, retrain, tune, validate, calibrate, and explain machine learning models for account and contact scoring. Present business impact to nontechnical customers, partner with MLOps to deploy solutions, and convert recurring insights into reusable product capabilities.

Develops AI agents, automation tools, predictive models, and decision-support solutions using structured and unstructured data. Leads AI projects from conception through deployment, evaluates analytical approaches, and collaborates with engineers, architects, product managers, and business stakeholders. Applies statistical and mathematical techniques, develops performance metrics and reports, communicates technical findings, and uses Python, SQL, and cloud technologies to solve complex business problems.

Own data science for customer-facing product features, including LTV prediction, experimentation, benchmarking, and forecasting. Proactively analyze data to identify customer problems and product opportunities, build and deploy production-grade models, and influence product roadmaps. Partner with Product, Engineering, and Analytics, communicate findings to varied audiences, iterate on shipped features, and lead new data-driven initiatives.

Lead data science and advanced analytics initiatives supporting underwriting, operational performance, and risk management. Develop, validate, deploy, and monitor explainable predictive models in a regulated insurance environment. Translate business needs into scalable analytical solutions, partner with governance and risk stakeholders, lead data scientists and analysts, and communicate insights to senior leaders. The role also supports enterprise data platforms, modern architectures, responsible AI, regulatory compliance, and data-driven organizational change.

Leads marketing measurement strategy and develops scalable frameworks for attribution, Marketing Mix Modeling, incrementality testing, experimentation, forecasting, and predictive analytics. Builds unified marketing data models, governs KPIs and tracking standards, creates executive reporting, and translates complex analyses into business recommendations. Partners cross-functionally with marketing, engineering, IT, privacy, and analytics teams while evaluating measurement technologies and vendors. Acts as the subject matter expert and helps scale the marketing analytics and data science function.

Build and own production-grade generative AI applications for legal technology, specializing in RAG, agentic systems, advanced search, embeddings, reranking, evaluation, and LLM workflows. Responsibilities include architecting modular Python applications, refactoring legacy code, implementing testing and observability, optimizing performance and costs, ensuring resilience and security, reviewing code, and supporting deployments and incident resolution.

Leads the architecture, development, and production delivery of AI platforms and applications. Establishes reusable RAG, agentic, evaluation, observability, and governance capabilities; mentors AI, ML, and data science teams; and partners with product and business stakeholders on AI strategy and roadmaps. Oversees responsible AI, performance, reliability, scalability, and cost optimization while guiding complex cross-functional initiatives from concept through ongoing improvement.

Build and operate production calibration and image-processing pipelines for multispectral infrared satellite imagery. Translate calibration algorithms into reliable software with testing, monitoring, alerting, diagnostics, CI/CD, and observability. Analyze pre-launch and on-orbit data, validate calibration coefficients, manage uncertainty budgets, and support calibration campaigns for FireSat and SBEM missions. Collaborate with scientists, instrument engineers, and product teams to ensure radiometric integrity and scientific quality of data products.

Develop and productionize algorithms that transform multispectral infrared satellite imagery into derived geospatial data products. Build scalable Python-based processing pipelines, validate scientific quality, document product designs, and integrate operational services for customer-facing applications. Collaborate with scientists, software and instrument engineers, product owners, and customers across wildfire, weather, and environmental monitoring missions. The hybrid role requires three days weekly onsite in Denver, Colorado.

Develop statistical analyses, experiments, predictive models, and machine learning solutions for Sales and B2B Marketing. Analyze large transaction-level datasets, evaluate business performance and product features, deliver stakeholder reporting and recommendations, and take data science projects from development through production. The role requires strong SQL, Python or R, advanced modeling expertise, and familiarity with consumer finance, sales operations, and B2B marketing.

Evaluate migration technologies, build quantitative models for migration risk and data quality, coordinate customer migration projects, develop data pipelines and analytical frameworks, implement Python and SQL validation workflows, assess cloud environments, and create dashboards and reporting for product, sales, and operations teams.

Develops and maintains Tableau dashboards and visual analytics products for public health decision-making. Analyzes complex datasets, identifies trends, translates findings into accessible visualizations, and collaborates with epidemiologists, programs, and stakeholders. Ensures data accuracy, privacy, suppression, confidentiality, governance, and dashboard sustainability while documenting data sources and business logic. Provides analytic support, communicates insights to leadership, and works remotely with public health partners. Up to 10% domestic travel may be required.

Lead client engagements from data onboarding through production, plan studies that demonstrate business value, and build automated machine learning workflows using Python, scikit-learn, pandas, and Spark. Translate client business questions into data solutions, manage stakeholder relationships, evaluate model performance, and inform product planning. Requires healthcare or life sciences data experience and end-to-end delivery of sophisticated data or machine learning solutions.

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Applied Data Scientist focused on incrementality, causal inference, experimentation, Marketing Mix Modeling, and predictive products. The role partners directly with customers to interpret data, diagnose measurement issues, explain statistical results, and provide actionable recommendations. Responsibilities include building diagnostic tools, analyzing model and experiment outcomes, improving methodologies and production capabilities, and collaborating with Product, Engineering, Media Strategy, Customer Success, and senior data scientists.