Top Data Science Jobs in Chicago, IL

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Leads fraud and risk-focused data science initiatives, using Python and advanced analytics to generate strategic insights and improve business performance. Manages client engagements, data pipelines, project execution, and teams while mentoring staff and resolving stakeholder issues. Responsibilities include building data architectures, developing visualizations, improving processes, and maintaining professional, technical, and independence standards.

Develop machine learning, deep learning, statistical, and Generative AI solutions using equipment and telematics data. Build models for anomaly detection, failure prediction, remaining useful life, and risk assessment. Support digital products through reliability monitoring, incident response, automation, connectivity planning, technical analysis, and performance optimization. Partner with engineers and data scientists on AI, analytics, IoT, and digital product initiatives.

Conducts quantitative research and experimentation for options market-making strategies. Responsibilities include building production testing pipelines, analyzing post-trade data, maintaining large tick-data datasets, presenting findings, and developing trading features and parameter configurations. The role requires learning options-market microstructure, exchange mechanics, networking, and FPGA-related concepts while collaborating with trading and engineering teams.

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.

The Data Scientist will analyze complex datasets, build predictive models, collaborate with stakeholders, and present insights for business decisions.

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.

Build and maintain pricing and profitability models to optimize lending strategies, incorporating proprietary credit models. Design A/B tests, analyze financial performance trends, and present data-driven recommendations to senior management. Collaborate with credit risk, capital markets, FP&A, marketing, and sales to quantify financial impact and improve pricing decisions.

Leads complex analytics and AI consulting engagements from opportunity shaping through solution design, model development, validation, deployment readiness, and monitoring. Provides technical leadership across machine learning, generative AI, LLMs, RAG, MLOps, and responsible AI. Partners with clients and internal teams to deliver business value, communicates insights to technical and business stakeholders, contributes to innovation and governance, and mentors junior colleagues. Travel is required 10–20% of the time.

Develop and optimize generative AI and large language model solutions, including fine-tuning, agentic workflows, retrieval-augmented generation, and experimentation. Research emerging algorithms, evaluate model performance using automated and human-in-the-loop methods, implement safety guardrails, and integrate AI services with cloud platforms. Mentor junior data scientists and guide technical approaches for AI-driven products.

Develop, deploy, and monitor fraud detection models and pattern-recognition pipelines using Python, SQL, machine learning, and large transactional datasets. Partner with Fraud Operations to refine models, reduce false positives and negatives, analyze emerging fraud trends, and improve underwriting and verification strategies. Communicate insights to senior stakeholders, apply AI in production, and lead team members and initiatives.

Develop and maintain Snowflake datasets and pipelines, analyze complex data, and contribute to statistical, machine learning, forecasting, NLP, and LLM initiatives. Create dashboards, visualizations, documentation, and presentations for stakeholders. Perform data preparation, feature engineering, validation, and quality governance while partnering with business, analytics, and technology teams to deliver scalable healthcare analytics solutions.

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

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independently structuring data, solving ambiguous problems, communicating recommendations clearly, and delivering measurable business impact.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, SQL or Python proficiency, strong communication, and ownership of ambiguous initiatives through measurable business impact.

Own the analytical function for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independent data work, strong analytical reasoning, persuasive communication, and ownership of ambiguous problems through measurable business impact.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence stakeholders and leadership decisions. The role requires independent data work, strong analytical thinking, persuasive communication, and ownership of ambiguous problems through measurable outcomes.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions and outcomes. The role requires independently structuring and interpreting data, communicating findings to leadership, and solving ambiguous problems using SQL or Python.

Own value-based bidding across digital marketing channels by building customer lifetime value models, integrating predictions with Google Ads and Meta, designing incrementality experiments, and advising Marketing on budget allocation, audience strategy, campaign measurement, and funnel performance. The role requires production-oriented machine learning, statistical analysis, automated data integrations, and collaboration with Pricing and Profitability teams.

Lead development and independent validation of advanced credit risk models used for lending criteria and verification. Prepare validation reports, monitor model performance, support production releases, and improve validation methodologies through automation, data manipulation, machine learning, and programming. Provide technical guidance and mentorship while communicating quantitative findings to technical and business stakeholders.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

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.

Supports the design, development, testing, and deployment of AI and machine learning solutions for business applications including forecasting, manufacturing optimization, demand planning, and automated decision-making. Builds prototypes, evaluates AI/ML frameworks, works with large datasets, documents best practices, monitors solution performance, researches emerging techniques, and communicates results to technical and non-technical stakeholders.