Top AI & Machine Learning Jobs in Chicago, IL

Lead data science and model risk initiatives by partnering with data scientists, engineers, and product managers. Build, evaluate, validate, and implement machine learning models using large-scale numeric and textual data. Apply statistical methods, backtesting, and model-risk expertise while translating complex analyses into business outcomes. Responsibilities also include researching emerging technologies, improving decision-making, and developing talent.

Build and deploy machine learning models for personal loan acquisition, including underwriting, pricing, verification, list selection, and campaign optimization. Analyze large-scale numeric and textual datasets using Python, Spark, AWS, H2O, and related tools. Collaborate with data scientists, engineers, and product managers, validate and backtest models, and translate statistical insights into business outcomes.

Leads data science and model risk initiatives, partnering with data scientists, engineers, and product managers. Builds, evaluates, validates, and implements machine learning models using large-scale data and technologies including Python, AWS, H2O, and Spark. Translates complex statistical findings into business outcomes, researches emerging methods, validates and backtests models, and develops talent. Requires quantitative education, substantial analytics experience, machine learning expertise, and relational database experience.

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.

Leads data science efforts for foundational AI and automation across a global payments network. Partners with data scientists, engineers, and product managers to develop scalable machine learning engines, AI agents, merchant intelligence, and risk solutions. Serves as an NLP and LLM domain expert, fine-tuning and deploying models using PyTorch, AWS, vector databases, and modern AI technologies. Translates complex ML work into business value while mentoring teams and delivering production-scale platforms and solutions.

Lead a product team building ML and GenAI infrastructure and model delivery for marketing and acquisitions. Define problem statements, set 3-year roadmaps tied to business outcomes, partner with data scientists and engineers, drive reusable scalable capabilities, influence cross-functional stakeholders, recruit and mentor talent, and measure success with data-driven metrics.

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.

Leads personal loan acquisition data science, developing and deploying machine learning models for customer targeting, campaign optimization, underwriting, pricing, and verification. Partners with data scientists, engineers, and product managers; analyzes large-scale numeric and textual data using Python, Spark, AWS, and related technologies; validates and backtests models; translates technical findings into business outcomes; and develops team talent.

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.

Lead model validation and risk quantification across business domains. Partner with data scientists, engineers, and PMs to build and challenge production models using Python, Spark, H2O, and cloud tools. Drive model governance, backtesting, and present identified model risks to executives while mentoring teams and advancing validation frameworks.