Top Data Science Jobs in Peoria, IL

Lead end-to-end data science and AI projects: translate business problems, prepare large datasets, build and deploy predictive/prescriptive and generative AI models (including LLM fine-tuning and prompt engineering), communicate insights to stakeholders, and promote responsible, explainable AI practices across consulting engagements.

Design and deploy optimization and machine learning models for allocating scarce parts and resources across competing programs. Responsibilities include formulating MILP and constraint optimization models, forecasting demand and supply risk, building data pipelines, validating outputs, communicating tradeoffs to stakeholders, mentoring junior data scientists, and partnering with software engineers to productionize scalable services.

Own end-to-end data science for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, analyses, experiments, reports, and recommendations. Translate complex findings into clear decisions, influence leadership, and drive measurable outcomes. Work independently with SQL or Python in a hands-on, remote-first environment.

Own end-to-end data science for growth, product, AI, automation, or partnerships. Define business problems, metrics, reports, analyses, and experiments; communicate insights and recommendations; and drive decisions and measurable outcomes with cross-functional stakeholders. The role requires independent analytical problem-solving, strong SQL or Python skills, persuasive communication, and ownership of ambiguous initiatives.

Lead design and evolution of large-scale nodal power market models. Define mathematical approaches, develop optimization/forecasting methodologies (MILP, SCUC/SCED), collaborate with engineers to productionize models, work with product and customers to translate market problems into software, mentor data scientists, and guide long-term product strategy.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

Drive revenue growth and advertiser value through data science for Nextdoor’s Ads Marketplace. Responsibilities include developing ranking and optimization models, designing experiments, analyzing auction performance, building explainability feedback loops, and delivering strategic insights. The role partners with Product, Engineering, Marketing, Sales, Finance, and Operations to improve advertising products and explore AI and large language model applications.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Lead data science for Mozilla’s advertising business by identifying high-impact opportunities, owning complex analyses, developing measurement and experimentation frameworks, analyzing revenue and performance, and translating findings into recommendations for technical partners and executives. Partner across Product, Engineering, Sales, Finance, and leadership to support business decisions in a fast-moving environment while balancing analytical rigor, pragmatism, and user privacy.

Lead data science partnership for Mozilla’s advertising business, identifying high-impact opportunities and owning complex analytics end to end. Develop measurement frameworks, experimentation strategies, instrumentation, and reporting for advertising products. Analyze revenue and performance, evaluate trade-offs, and communicate clear recommendations and uncertainty to technical teams and senior executives.

Designs, validates, and operationalizes predictive, machine learning, and artificial intelligence models using large structured and unstructured datasets. Performs statistical analysis, feature engineering, exploratory analysis, visualization, and clinical program outcome validation. Collaborates cross-functionally to integrate solutions, prepares end-to-end model pipelines, and communicates analytical findings to stakeholders to improve business processes.

Develops retention, churn, and customer lifetime value models using statistical analysis and machine learning. Responsibilities include extracting and preparing structured and unstructured data, performing ad hoc analyses, creating dashboards and reports, automating analytical procedures, monitoring implemented models, and presenting findings to cross-functional business, IT, and management teams.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Staff Data Scientist responsible for owning ambiguous, high-impact product and business problems; designing experiments; analyzing product data; developing analytical tools and frameworks; partnering with product, engineering, design, and leadership; and mentoring data scientists. The role requires advanced statistical expertise, strong product judgment, production programming experience, and the ability to translate complex analyses into strategic recommendations.

Conduct workforce analytics and applied organizational research on attrition, mobility, engagement, performance, and planning. Analyze and validate HR data using SQL, R, or Python; apply statistical testing and regression; translate business questions into actionable insights; and communicate findings through visualizations, reports, and presentations. Partner with HR stakeholders while maintaining the accuracy, documentation, confidentiality, and integrity of sensitive workforce data.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Leads analytical and scientific strategy for Fetch’s Play and Rewarded Apps businesses. Develops predictive and machine learning models, experimentation frameworks, causal analyses, feature pipelines, and measurement systems using Python and SQL. Partners with Product, Marketing, Engineering, and business leaders to improve monetization, engagement, retention, personalization, and marketplace outcomes. Translates complex findings into strategic recommendations, mentors data scientists and analysts, and establishes analytical best practices.

Lead advanced research and predictive modeling on residential housing and energy performance data. Apply causal inference, anomaly detection, regression, and model validation to improve Pearl’s SCORE and energy models. Develop new performance metrics, evaluate climate-risk data, collaborate with external research partners, and communicate findings through white papers, publications, and clear stakeholder explanations.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.

Design, deploy, and manage machine learning models supporting client acquisition, retention, and reactivation. Develop Next Best Action, CRM, paid media, and personalization solutions using LLMs and traditional ML. Design and interpret experiments, analyze large-scale datasets, communicate recommendations to stakeholders, and collaborate with product, design, and engineering teams on product and infrastructure roadmaps.

Own end-to-end data science for emerging content surfaces, including product experimentation, causal inference, exploratory analysis, metrics, dashboards, and strategic insights. Identify opportunities to grow content supply, creators, and engagement while improving discovery. Lead content data architecture and quality initiatives, develop source-of-truth assets, and apply advanced statistical methods such as quasi-experiments and network testing. Partner with Product, Engineering, Design, and Research to influence product direction and communicate findings to diverse audiences.

Develop statistical models, experiments, causal inference methods, and scalable analytical workflows to generate business insights. Partner cross-functionally with Client Experience, Strategy, Marketing, Styling, Support, and Engineering teams to define metrics and improve products. Own analytical initiatives from framing through delivery, communicate executive-ready recommendations, mentor data scientists, and leverage LLMs and AI agents to accelerate analysis and workflows.