Top Data Science Jobs in Chicago, IL

Build, validate, and implement machine learning models for Discover Card portfolio underwriting. Analyze large numeric and textual datasets using Python, AWS, Spark, H2O, and related technologies. Partner with data scientists, software engineers, and product managers to deliver customer-focused products. Apply statistical modeling, classification, clustering, sentiment analysis, time series, deep learning, and big-data analysis while translating complex findings for stakeholders.

Develop and refine credit risk, pricing, and predictive models for consumer lending. Design A/B testing frameworks, analyze model performance, collaborate with strategy and operations teams, and present data-driven recommendations to senior management. The role requires statistical analysis, machine learning, Python, SQL, independent problem-solving, and effective communication across teams.

Leads alternative data strategy for credit decisioning by evaluating vendors and datasets, engineering features, building and validating machine learning models, and developing scalable processing and monitoring systems. Partners with data scientists, software engineers, and product managers while translating complex analyses into business outcomes. The role requires expertise in large-scale data analysis, statistical modeling, machine learning, and relational databases, with hybrid office attendance.

Develop predictive risk management, business intelligence, and statistical solutions for financial services clients. Lead descriptive, predictive, and prescriptive analytics using large datasets and techniques including regression, segmentation, gradient boosting, survival analysis, and PCA. Program in R, Python, and SQL, present recommendations to executives and customers, support proposals and sales materials, mentor junior colleagues, and manage client engagements with limited supervision.

Develop predictive models and advanced analytics for credit risk, fraud, insurance, and marketing applications. Lead analytics engagements from design through implementation, validation, and client presentation. Analyze large datasets using statistical and machine learning techniques, build scalable analytical frameworks, collaborate with clients and internal stakeholders, and translate findings into actionable business recommendations. The hybrid role also supports consulting, proposals, and business development.

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 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 data science and machine learning initiatives for payment-network applications, including fraud, risk, merchant acceptance, intelligence, and monitoring. Partners with data scientists, engineers, and product managers to deliver customer-focused products. Builds models through design, training, evaluation, validation, and implementation while translating technical findings into business outcomes and developing team talent.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Build and implement machine learning models for personal loan acquisition, including customer selection, campaign optimization, underwriting, pricing, and verification. Partner with data scientists, engineers, and product managers; analyze large-scale numeric and textual datasets using Python, Spark, AWS, and related tools; validate and backtest models; and translate statistical insights into customer-focused business outcomes.

Develop predictive models and advanced analytics for credit risk, fraud, insurance, and marketing use cases. Lead client analytics engagements from design through implementation and presentation, analyze large-scale datasets, build scalable analytical frameworks, and translate findings into business recommendations. Collaborate with clients, sales teams, and internal stakeholders while supporting proposals and analytics consulting.

Develop and validate clinical heuristics and algorithms using real-world healthcare data. Build Python profiling pipelines, automated data-quality checks, and longitudinal patient cohorts. Analyze complex clinical databases and ETL pipelines, evaluate AI, NLP, and heuristic outputs, and collaborate with clinical, labeling, engineering, and data science teams. Maintain documentation, configuration, metrics, and validation standards for transparent, high-quality clinical analytics.

Leads health economics and outcomes research and real-world evidence projects for pharmaceutical, biotech, academic, and other partners. Designs and analyzes pharmacoepidemiologic studies, treatment patterns, healthcare utilization, causal inference, and survival analyses. Builds reusable analytical infrastructure, collaborates with scientists, engineers, and product teams, interprets results for stakeholders, supports publications and regulatory work, and ensures compliance with data governance and applicable regulations.

Develop, deploy, and monitor fraud detection models and pattern-recognition pipelines across lending products. Analyze large transactional datasets using Python and SQL, partner with Fraud Operations to refine models, reduce false positives and negatives, improve underwriting and verification, apply AI in production, and lead and mentor team members.

Leads data science initiatives using machine learning, statistical modeling, and large-scale data analysis. Partners with software engineers and product managers to develop customer-focused products, builds and validates models across the full development lifecycle, translates technical findings into business outcomes, evaluates emerging technologies, and develops data science talent. Requires substantial experience with programming, machine learning, relational databases, cloud computing, and people management.

Leads data science and machine learning initiatives for payment-network applications, including fraud, risk, merchant intelligence, and monitoring. Partners with data scientists, engineers, and product managers to build scalable models and AI tools. Responsibilities span model design, training, evaluation, validation, and implementation using Python, AWS, H2O, Spark, and other technologies. The role also requires stakeholder communication, innovation, and team talent development.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Analyze and improve production fraud models and rules across payment methods and merchant segments. Identify emerging fraud patterns, recommend features and decision strategies, assess label quality, design experiments, evaluate third-party fraud vendors, and communicate model performance and tradeoffs to technical, operational, commercial, and compliance stakeholders.

Lead real-world evidence studies using complex clinical and molecular data, observational study design, survival analysis, causal inference, and machine learning. Collaborate with pharmaceutical partners, derive clinical endpoints, interpret results, develop reusable analytical code, and apply LLM-based tools to accelerate workflows. Communicate findings to technical and non-technical stakeholders through reports, presentations, manuscripts, and conference materials while partnering cross-functionally to improve data quality and analytical practices.

Lead data science for Growth Marketing by developing attribution, lifetime value, acquisition cost, retention, and predictive models. Design and analyze experiments, including A/B tests, geo-tests, holdouts, and incrementality studies. Partner with Growth, Finance, Product, analytics, and engineering teams to optimize marketing spend, establish data pipelines, and create executive dashboards. Translate complex analyses into strategies that improve user acquisition, retention, and profitability.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, experiments, and recommendations that drive decisions and measurable outcomes. Partner with cross-functional stakeholders, communicate insights clearly, and independently solve ambiguous problems using SQL and Python.

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 product, growth, finance, and operations decisions. The role requires strong analytical skills, independent data work, persuasive communication, and ownership of ambiguous problems with measurable business impact.

Own end-to-end data science and analytics for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires strong SQL or Python skills, first-principles problem solving, independent data interpretation, persuasive communication, and ownership of ambiguous business problems.