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.

Analyzes complex data sets, develops Python-based data solutions and pipelines, and applies advanced analytics to support client strategy and business performance. Designs dashboards and visualizations, collaborates with cross-functional teams, manages stakeholders, improves data quality, and builds client relationships. The role also involves solving complex business challenges, identifying industry trends, and delivering high-quality work within PwC’s Risk & Regulatory practice.

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.

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.

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.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Build and validate predictive models, analyze large structured and unstructured datasets, generate business insights, present analytic results to stakeholders, and develop tools to advance analytics capabilities.

The Senior Associate, Data Scientist will partner with cross-functional teams to identify risks associated with models and validate machine learning models across the enterprise, enhancing decision-making while ensuring model governance and risk management.

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.

Leads data science and advanced analytics initiatives for Auto Claims by developing, evaluating, and implementing machine learning and predictive modeling solutions. Integrates structured and unstructured data, researches emerging techniques, communicates model results to stakeholders, and manages moderately complex projects. Provides initial mentoring and development support to junior data scientists while helping improve business decisions and outcomes.

Leads advanced analytics, machine learning, statistical modeling, and NLP solution development using healthcare and operational data. Builds scalable pipelines, visualizations, and deployed models on Google Cloud Platform, translates findings for clinical and executive leaders, designs experiments, and establishes performance metrics. Requires collaboration across clinical, technical, and data engineering teams, with occasional on-site travel for executive presentations.

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.