Top Machine Learning Jobs in Chicago, IL

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.

Design, train, validate, and deploy machine learning and deep learning models for e-procurement use cases including prediction, ranking, recommendations, entity resolution, document intelligence, and automation. Perform exploratory data analysis, feature engineering, experimentation, model evaluation, and error analysis. Build maintainable Python and SQL workflows, collaborate with engineering and product teams, and communicate technical findings to diverse stakeholders.

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.

Lead a data science team developing scalable AI and NLP solutions for customer-facing financial applications. Partner with data scientists, engineers, product managers, and designers to build, train, evaluate, fine-tune, and operationalize machine learning and large language models. Drive research into generative AI, training optimization, self-supervised learning, explainability, and RLHF while delivering production systems at scale. Translate technical findings into business outcomes and develop team talent.

Develop data science and analytics solutions for connected Caterpillar equipment. Responsibilities include gathering, mining, and processing large datasets; building data models; applying NLP, text analysis, machine learning, and statistical methods; defining analysis requirements; communicating insights through visualization; and optimizing algorithms. The role requires Python, database access, cloud technology, version control, and strong communication skills.

Lead the technical implementation, architecture, adoption, and enablement of ServiceNow’s agentic AI capabilities. Build solutions, proofs of concept, demonstrations, and AI Impact Accelerators; advise customers and internal stakeholders; guide enterprise architecture decisions; develop training and technical content; and collaborate with sales, product, marketing, engineering, and AI strategy teams. The role also drives innovation, roadmap development, value realization, and thought leadership across the Customer Excellence organization.

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.

Lead Adyen’s Personalize Intelligence team developing machine-learning systems for real-time Checkout optimization and AI products. Provide technical leadership on architecture, reliability, and scalable services; coach machine learning scientists and data engineers; define goals and product roadmaps with Product; collaborate across Design, Engineering, Data, and leadership; monitor system performance; foster an inclusive, high-performing culture; and recruit new team members.

Leads AI technology integration and deployment within the tax practice. Oversees data infrastructure, pipelines, modeling, quality validation, and multi-source integration. Develops Python-based analytics solutions using machine learning and natural language processing, conducts rapid experimentation, and drives data-informed innovation. Coaches and develops high-performing teams while collaborating with stakeholders and advising on strategic initiatives.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Leads the design, development, deployment, and optimization of scalable AI and generative AI solutions. Translates business needs into secure, production-ready architectures; applies foundation models, NLP, retrieval, vector search, and agentic workflows; and implements MLOps and LLMOps practices. Manages project workstreams, evaluates solution performance, ensures security and compliance, collaborates with stakeholders, and coaches junior team members.

Analyzes workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

Develop and implement generative AI solutions for client engagements, including contact center applications, machine learning and NLP integrations, prompt engineering, LLM evaluation, and LLMOps governance. Use Python, Azure AI Services, Google Vertex AI, LangChain, and LLM APIs to build, deploy, monitor, and refine AI applications while supporting data analytics and client outcomes.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

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 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.

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.

The Senior Data Scientist supports Customer Success through deep-dive analysis, predictive modeling, causal inference, experimentation, and KPI development. The role analyzes churn, retention, renewals, upsell opportunities, and revenue trends; diagnoses metric shifts; builds data pipelines and dashboards; and develops AI/LLM-powered solutions. The scientist collaborates with stakeholders, communicates insights, improves data quality, and measures the impact of customer success initiatives and product changes.

Provides global technical leadership for Physical AI and cyber-physical product assurance. Develops standards, certification programs, assessment methodologies, testing criteria, and customer-facing services for robotics, autonomy, drones, smart mobility, and connected intelligent products. Evaluates real-world system behavior, hazards, failure modes, safety controls, human interaction, robustness, and lifecycle changes. Advises customers, regulators, and standards bodies while building global evaluator training, delivery guidance, and scalable assurance capabilities.

Provides global technical leadership for digital AI standards, certification, assurance, training, advisory, and assessment services. Develops AI governance and lifecycle assurance methodologies covering predictive and generative AI, LLMs, agents, cloud AI, model risk, testing, transparency, fairness, privacy, security, robustness, and operational resilience. Translates regulations, standards, emerging technologies, and customer needs into scalable offerings while engaging customers, regulators, standards bodies, and internal stakeholders.

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 GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

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.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Owns Attain’s data science methodology, including causal inference, attribution, incrementality, media mix modeling, audience targeting, and modeled-data validation. Defines defensible measurement standards, partners with Product, Engineering, ML, and commercial teams, presents methodology to clients and partners, manages external consultants, and leads a team of data scientists. The role requires deep AdTech/MarTech experience, strong statistical and ML expertise, executive communication skills, and experience with large-scale consumer data.