Top Remote Data Science Jobs in Portland, ME

Measures customer engagement impact on pipeline, renewals, account health, and predictive model performance. Conducts baseline, holdout, before-and-after, and control-group analyses; validates customer data and helps tune models. Builds reusable value analyses, dashboards, and reporting for leadership, Customer Success, and Data Science. Requires strong SQL, Python, statistics, experiment design, business communication, and experience translating messy data into trusted insights.

Develop and standardize analytical methods for inferring residence, displacement, and poverty from mobile network operator data. Assess data quality, write technical specifications and tests, guide operator engineering teams, tune statistical methods, and balance accuracy with implementation constraints. Design privacy-preserving analyses, improve data security practices, train internal teams, and support international workshops and emergency-response programs.

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.

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.

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.

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.

Analyzes structured and unstructured datasets supporting U.S. Navy electronic warfare systems. Develops statistical, machine learning, NLP, data modeling, visualization, and reporting solutions to improve system readiness, reliability, maintainability, operational availability, and lifecycle performance. Integrates logistics, engineering, maintenance, and operational data; identifies trends and risks; and presents data-driven findings to technical and program stakeholders.

Develops analytical models and applications for U.S. Navy electronic warfare and sustainment systems. Applies data mining, statistical analysis, NLP, machine learning, and data modeling to structured and unstructured data. Integrates logistics, engineering, maintenance, reliability, and operational data to assess readiness, performance, risks, and availability. Creates visualizations, dashboards, reports, and briefings, while collaborating with engineers, logisticians, operations researchers, and program personnel to support lifecycle management and data-driven decisions.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Develops data science models, analytical processes, visualizations, dashboards, and reports for U.S. Navy electronic warfare systems. Integrates logistics, engineering, maintenance, reliability, and operational data to evaluate system readiness, reliability, maintainability, performance, risks, and lifecycle sustainment. Applies data mining, statistical analysis, NLP, and machine learning to support operational decisions and collaborates with engineers, logisticians, analysts, and program personnel.

Own product experimentation, causal analysis, KPI frameworks, predictive modeling, and user segmentation to improve growth, engagement, retention, and revenue. Partner with Product, Engineering, and Design to translate ambiguous questions into rigorous analyses and launch recommendations. Develop lifecycle intervention intelligence, evaluate treatment effects and persistence, communicate uncertainty, and influence roadmap decisions through reusable analyses and clear narratives.

Own company-wide KPI, modeling, and measurement workstreams; develop trusted metrics, KPI trees, forecasts, causal analyses, scenarios, and diagnostic tools. Partner with Data Engineering, Product, Engineering, Finance, and Commercial teams to connect business drivers to retention, monetization, revenue, and margin. Build reproducible SQL and Python workflows, communicate uncertainty and assumptions, and drive adoption of analytical frameworks and recommendations.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes trusted metrics, partners with Finance, Product, Commercial, Engineering, and Data Engineering, and mentors scientists. The role drives adoption of governed self-service analytics and communicates assumptions, uncertainty, and business trade-offs to leadership.

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Own projects from prototyping through production, conduct experiments, explore large-scale data, and collaborate with product, engineering, analysts, and account teams to improve attribution and marketing ROI.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Lead analytics for client engagements across data onboarding, study planning, predictive modeling, production deployment, and ongoing optimization. Develop models for audience targeting, segmentation, propensity scoring, and campaign optimization, primarily for commercial pharmaceutical applications. Partner with business, analytics, and IT stakeholders, lead technical workshops, present findings to senior audiences, and manage client relationships. Build machine learning pipelines using Python, SQL, and cloud technologies, while contributing client insights to internal product development.

Develop revenue optimization and risk mitigation strategies using experimentation, causal inference, forecasting, Monte Carlo simulations, and machine learning. Build data pipelines, improve analytics infrastructure, generate insights for client-facing teams, and influence Product and Engineering roadmaps. The role requires strong strategic thinking, business impact orientation, and collaboration in a remote-first environment.