Top Data Science Jobs in Chicago, IL

Develops statistical and machine learning models for kinematic behavior detection using live, historical, time-series, and geospatial data. Responsibilities include data integration, ETL, feature engineering, forecasting, anomaly detection, algorithm evaluation, simulation, visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to mission-focused software applications, APIs, data workflows, code reviews, troubleshooting, and agile development in a remote-first environment.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

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.

Early-career Data Scientist supporting supply chain transformation through machine learning, forecasting, statistical modeling, data preparation, dashboards, and AI solutions. The role addresses inventory optimization, demand forecasting, production planning, delivery performance, and cost reduction. Responsibilities include developing analytical datasets and workflows with Python and SQL, evaluating models, integrating enterprise data, supporting cloud-based analytics, and communicating insights to business stakeholders in a manufacturing environment.

Leads the full machine learning lifecycle for foundational personalization and recommendation models, including client and item representations, embeddings, retrieval, ranking, and generative approaches. Designs experiments, evaluates business impact, deploys and operates production models, and works with large-scale behavioral, transactional, textual, and visual data. The role also involves technical leadership, cross-functional collaboration, mentoring, and establishing modeling and engineering best practices.

Own end-to-end analytical functions for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and influence product, growth, finance, and operations decisions. The role requires strong independent data analysis, first-principles problem solving, clear communication, and ownership of ambiguous business challenges.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions and metrics, build reports, analyze data, design experiments, communicate insights, and drive decisions and outcomes with cross-functional stakeholders. The role requires strong analytical thinking, independent problem-solving, persuasive communication, and end-to-end ownership of ambiguous business challenges.

Own end-to-end analytical work for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, experiments, and analyses; surface insights; communicate recommendations; and influence product, growth, finance, and operations leaders to drive measurable outcomes.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations leaders. The role requires strong SQL or Python skills, independent analytical problem-solving, clear communication, and ownership of ambiguous, high-impact initiatives.

Support model risk governance by maintaining policies, documentation, inventories, validation plans, training, and stakeholder communications. Develop statistical programs and analytical solutions using R, Python, SQL, Hive, Spark, and cloud platforms to analyze large datasets and support credit risk and business intelligence consulting engagements.

Develop marketing effectiveness analytics using Marketing Mix Modeling, attribution, customer lifetime value, experimentation, and causal inference. Build automated, scalable model pipelines, insight-ready datasets, and self-serve dashboards. Analyze campaigns, support marketing strategy, communicate findings to executives, and lead high-impact projects independently while collaborating cross-functionally.

Own analytical strategy and develop AI/ML models for Zip’s financial products, including cash and rent advances. Lead ambiguous 0-to-1 initiatives using bank transaction data, establish evaluation frameworks, guide risk and growth decisions, and deliver models from experimentation through production. Provide technical direction to data scientists, mentor junior team members, collaborate with engineering, and advise senior stakeholders on model trade-offs. The role requires strong financial services, statistical, research, and machine learning expertise.

Designs and deploys predictive models, segmentation frameworks, machine learning solutions, and data pipelines supporting marketing performance and customer analytics. Analyzes complex datasets, conducts experiments, validates and monitors models, and communicates insights to technical and non-technical stakeholders. Partners with strategy, media, client services, and engineering teams to create scalable data-driven solutions while contributing to peer review, knowledge sharing, and evolving data science practices.

Develops and deploys analytical and machine learning solutions, leading exploratory data analysis, statistical modeling, feature engineering, scalable data pipelines, validation frameworks, and model optimization. Collaborates with product and engineering teams on proofs of concept and production integration, evaluates emerging technologies, and communicates insights through visualizations and presentations. Requires Python, SQL, cloud or containerized environments, ML pipeline experience, U.S. citizenship, and the ability to obtain and maintain Public Trust clearance.

Lead and build Neural Earth's geospatial data science function: design and deploy Python-based geospatial models, architect production geospatial data pipelines, validate reproducible methods, translate scientific outputs into customer-ready products, and manage and mentor at least one direct report while advising customers and stakeholders.

Develops machine learning models and production data science pipelines to improve subscription conversion, content recommendations, user understanding, workflow automation, and product decisions. Leads analytical projects and experiments, applies statistical methods to web-scale data, and collaborates with data, product, growth, and engineering teams. The role also communicates complex findings to diverse audiences and helps foster a strong data science culture.

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.

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.

Develop statistical methodologies and production data science solutions for consumer purchase panel products. Apply sampling, weighting, modeling, classification, anomaly detection, and optimization to improve data quality and representativeness. Build scalable Python and SQL workflows, monitoring frameworks, and internal tools. Collaborate with Product, Engineering, Data Operations, and business partners throughout the project lifecycle, while clearly communicating methodologies, findings, assumptions, and tradeoffs.

Develop and deploy AI and machine learning models for production optimization, quality control, anomaly detection, predictive maintenance, supply chain forecasting, and operational analytics. Build scalable data pipelines, MLOps processes, APIs, dashboards, and LLM-powered solutions using prompt engineering, RAG, fine-tuning, and AI agents. Analyze complex datasets, conduct statistical experiments, monitor model performance, and promote responsible AI practices.

Senior Data Scientist responsible for partnering with business leaders to frame ambiguous problems, deliver statistical and machine learning solutions, communicate insights, and drive measurable adoption. The role includes end-to-end analytical ownership, stakeholder influence, executive communication, Agile backlog management, cross-functional collaboration with engineering and technology teams, mentorship, governance, and improving analytics maturity.

Leads epidemiological research and data science projects focused on infectious disease prevention. Designs studies, statistical models, and reproducible data pipelines; integrates clinical, biospecimen, administrative, and external datasets; oversees data quality and provenance; develops predictive and spatial models; interprets and visualizes findings; contributes to manuscripts, grants, and presentations; and mentors research staff and learners. Collaborates with clinicians, researchers, technical teams, external institutions, and community partners.

Leads data science research for a breast cancer program, developing multimodal clinical data frameworks, data dictionaries, machine learning pipelines, statistical models, and reproducible genomic analyses. Collaborates on computational tools, manages projects, drafts grants, creates training materials, supervises trainees, and supports complex data requests involving clinical, genomic, pathology, imaging, and administrative datasets.

Build and validate predictive lending models, analyze consumer risk and portfolio impact, implement and monitor models with engineering, develop tooling and automated SQL/Python dashboards, design A/B tests, and communicate actionable insights to senior management.

Develop and deploy LLM and machine learning models for Airbnb Community Support. Responsibilities include automating LLM evaluation, curating synthetic datasets, analyzing customer issues, identifying model failure modes, building personalization models, exploring data, prototyping solutions, and communicating insights to cross-functional stakeholders. The role focuses on scalable, production-ready AI products and operational optimization.