Top Hybrid Data Science Jobs in Chicago, IL

Leads Caterpillar’s Condition Monitoring Analytics Transformation team and portfolio. Manages 8–12 data scientists while providing hands-on technical guidance on machine learning, AI, predictive maintenance, failure modeling, evidence clustering, persona modeling, and closed-loop learning. Collaborates with commercial, product, engineering, R&D, and MLOps teams to deploy scalable analytics solutions, prioritize resources, drive innovation, and develop team talent.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Build and deploy machine learning models and scoring platforms for credit decisioning and valuation. Analyze large-scale financial and textual datasets using statistical modeling, cloud technologies, and open-source tools. Responsibilities include model design, training, validation, implementation, analytical tool development, alternative data analysis, and collaboration with software engineering and product teams to deliver scalable credit products.

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.

Develop predictive risk management and business intelligence solutions for financial services clients. Lead descriptive, predictive, and prescriptive analytics using large datasets and statistical techniques. Program in R, Python, and SQL, present insights to executives and customers, support proposals and product adoption, and mentor junior colleagues. The role is hybrid and requires approximately 10% to 20% travel.

Leads a strategic insights team analyzing product development productivity, operating models, workflows, and AI adoption. Frames hypotheses, conducts quantitative analysis and experiments, evaluates delivery and business outcomes, and translates findings into executive recommendations. Partners with Product Operations, Technology, Product, and Finance to implement actions, track adoption, and measure results. Establishes analytical standards, communicates with senior leaders, and develops a high-performing analytics team.

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 advanced algorithms and apply statistical, data mining, machine learning, and AI techniques to large structured and unstructured datasets. Identify trends, evaluate emerging data technologies, and communicate analytical findings to clients and ZS stakeholders. The role requires collaboration across multiple projects, strong quantitative and research skills, and proficiency with Python, SQL, Pandas, and PySpark. Client-facing work may require travel.

Lead Data Scientist partners with business leaders to identify opportunities, design, develop, and deploy advanced analytics and ML solutions. Responsibilities include data preparation, modeling, feature engineering, delivering insights and visualizations, establishing standards, and directing associate data scientists, contractors, and consultants to produce measurable business value.

This role involves onboarding clients, solving scientific problems, guiding data analysis on the Tempus platform, and creating documentation. Requires expertise in biomedical datasets and programming.