Top Senior Data Science Jobs in Chicago, IL

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.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

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.

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Lead the design, development, and operation of scalable data pipelines, trusted data products, metrics, and feature-ready datasets. Establish data quality, governance, orchestration, testing, observability, and engineering standards using Python, PySpark, SQL, dbt, Airflow, and Databricks. Partner with analytics, product, and engineering teams to support AI adoption and productivity measurement, while mentoring a team of two and driving delivery through ambiguous, cross-functional initiatives.

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.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

Leads Rental Insights and Analytics by developing BI and analytics roadmaps, analyzing complex business data, defining KPIs, gathering requirements, and delivering actionable insights. The role applies Python, SQL, statistical methods, machine learning, cloud data platforms, and visualization tools to optimize rental revenue, dealer operations, product utilization, and customer growth. It also requires collaboration with senior leadership and product teams, data storytelling, and strong technical problem-solving.

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

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.

Own end-to-end data science for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence leadership decisions. The role requires independent analytical work, strong SQL or Python skills, first-principles problem solving, persuasive communication, and ownership of ambiguous problems. Candidates need 6+ years of experience in consulting, investment banking, or high-growth technology.

Own end-to-end data science for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations; and drive decisions and measurable outcomes with cross-functional stakeholders.

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.

Develop clinically grounded data science and AI solutions using clinical, laboratory, commercial, and enterprise data. Responsibilities include machine learning and statistical modeling, generative AI, literature intelligence, exploratory analysis, feature engineering, validation, documentation, and deployment support. The role leads analytical workstreams and research collaborations, partners with clinical and business stakeholders, and communicates findings through reports, presentations, and decision-support tools while maintaining reproducibility, privacy, security, and audit-ready practices.

Manage NASA aerospace portfolio data through cataloging, metadata standards, taxonomies, controlled vocabularies, data lineage, provenance, classification, and quality documentation. Evaluate and implement an enterprise data catalog, integrate it with Dagster, establish metadata and freshness metrics, onboard and retire datasets, and drive adoption across technical and program teams. Translate between data professionals and program stakeholders while improving data discoverability, trust, and usability.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Analyzes financial and ERP data using statistical analysis, data mining, predictive modeling, and advanced algorithms. Supports Oracle financial shared services, data conversion, business continuity, disaster recovery, and contingency planning. Develops analytical outputs that improve agency KPIs and identifies trends and business opportunities. Requires collaboration with systems, network, hosting, and disaster recovery teams, plus participation in COOP exercises and Oracle environment upgrades.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Serve as Netflix Animation Studios’ founding data scientist, developing measurement frameworks, forecasting and scenario-planning models, dashboards, and data products for slate planning, production efficiency, workforce allocation, and strategic decision-making. Partner with studio leadership, data scientists, analytics engineers, and data engineers to establish data foundations, define metrics, analyze operational drivers, and translate complex findings into actionable recommendations.

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.