Top Remote Data Science Jobs in Peoria, IL

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.

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.

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.

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.

Build and deploy applied data science models for auto insurance carrier matching, media buying, bidding, and ad serving. Own the full modeling lifecycle, including data discovery, validation, deployment, monitoring, and adaptation to late labels, sparse sessions, feedback loops, and market drift. Partner with business stakeholders and an ML engineer to scale production models and deliver measurable acquisition and ROAS outcomes.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Leads people analytics research using causal inference, quasi-experimental methods, statistical modeling, and large workforce datasets. Partners with Recruiting, Compensation, Talent, Legal, and business leaders on hiring, retention, promotion, pay equity, quality-of-hire, and organizational effectiveness. Translates research into visualizations and actionable decisions, leads compensation analytics and governance reporting, and uses AI tools to accelerate analysis and synthesis.

The Analytics Manager analyzes complex datasets, builds models, conducts scenario analyses, and provides strategic recommendations to guide decision-making for leadership.

Conduct complex actuarial and predictive analyses supporting insurance product development, pricing, rating, reserving, financial analysis, and process improvements. Develop large-scale predictive models, multivariate analyses, SQL queries, tools, workflows, and business processes. Collaborate with actuaries, product managers, and business teams, communicate insights, improve efficiency, and mentor less experienced staff.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.