Top Machine Learning Jobs in Chicago, IL

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

AI internship supporting business and technology initiatives through requirements analysis, data analysis, process optimization, stakeholder collaboration, project documentation, testing, and troubleshooting. The role applies artificial intelligence, machine learning, generative AI, NLP, and automation tools to improve business operations and decision-making. Interns gain experience with responsible AI, risk management, Agile delivery, business architecture, functional analysis, and insights reporting in a hybrid Chicago-based banking environment.

Build and operate scalable data pipelines, evaluation frameworks, and production AI/ML/NLP models. Own the full lifecycle from data collection and transformation through training, deployment, monitoring, and continuous improvement. Develop robust tooling, observability, and quality checks while collaborating with product and engineering to integrate AI capabilities into user-facing features. Work pragmatically in a fast-moving startup environment and translate business needs into effective data and machine learning solutions.

Leads enterprise Responsible AI governance, overseeing AI and Generative AI risk assessments, fairness testing, model evaluations, monitoring, controls, and lifecycle governance. Advises business, technical, legal, compliance, and risk stakeholders; develops scalable governance frameworks; and evaluates emerging technologies such as foundation models and agentic AI. Drives controls for monitoring, human oversight, intervention, containment, and system shutdown while balancing regulatory obligations, risk management, innovation, and business value.

Develop and deploy scalable AI and machine learning applications, including forecasting, anomaly detection, and natural language processing solutions. Design software architectures, troubleshoot full-stack issues, participate in technical design sessions, and collaborate with cross-functional teams. The role requires production machine learning experience, strong programming skills, and the ability to learn evolving AI technologies. Preferred experience includes web service frameworks, distributed systems, and Azure cloud computing.

Lead forensic technology engagements by designing and deploying machine learning models, statistical algorithms, and data pipelines for digital investigations and electronic discovery. Collaborate with clients to define goals, analyze cloud and enterprise data, develop reusable automation components, communicate findings to executive audiences, and deliver training. The role also involves managing people on high-profile consulting engagements and providing client-facing technical leadership.

Leads responsible AI and model governance while building and deploying healthcare fraud, waste, and abuse models. Establishes standards for documentation, monitoring, bias, drift, validation, and production readiness. Evaluates generative AI use cases, develops policies, validates large-scale claims models, and presents methodology and controls to partners, auditors, and investigators. Works remotely with data scientists, BI developers, and healthcare subject-matter experts.

Supports the design, development, testing, and deployment of UiPath RPA and agentic AI automation solutions. The intern integrates AI agents, bots, APIs, databases, applications, and enterprise systems; analyzes business processes; develops proofs of concept; documents workflows and technical specifications; and assists throughout the software development lifecycle. The role also involves responsible AI, security, governance, monitoring, troubleshooting, and collaboration with technical and non-technical stakeholders.

Build AI systems for legal teams by designing legal datasets, annotating training and benchmarking data, defining model requirements, translating legal workflows into structured processes, validating product capabilities, engaging clients, and prototyping with AI tools and scripting. Requires a JD and active or prior bar membership, 2–8+ years of legal practice, strong familiarity with litigation, investigations, compliance, or e-discovery workflows, and comfort collaborating with engineers and product teams.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Lead client-facing AI program governance and delivery as PMO manager. Translate business objectives into AI roadmaps and OKRs, oversee AI/ML lifecycle, ensure data readiness, model validation, deployment, and responsible AI compliance. Manage PMO standards, portfolio prioritization, AI-augmented reporting, and lead executive communications while mentoring PMs, TPMs, and analysts.

Develop and productionize machine learning models and predictive analytics using Python, Databricks, PySpark, SQL, and Delta Lake. Build scalable data science workflows, resolve healthcare data quality and entity-resolution issues, and apply Master Data Management practices. Partner with engineering, product, analytics, and business teams to deliver governed, HIPAA-compliant solutions. Perform exploratory analysis, feature engineering, model evaluation, deployment, documentation, and data lineage management for healthcare and member engagement initiatives.

Supports global supply chain and operations through data cleaning, exploratory analysis, machine learning forecasts, anomaly detection, KPI analysis, predictive modeling, and dashboard development. The intern uses Excel and Power BI to create reports and visualizations, identifies data quality issues, collaborates with cross-functional teams, and presents actionable recommendations to stakeholders and management.

Design, develop, and deploy scalable AI applications and services across the OneStream platform. Build production machine learning solutions, troubleshoot full-stack issues, participate in technical design sessions, and collaborate with cross-functional teams. The role involves applying forecasting, anomaly detection, and natural language processing while maintaining reliable, well-documented code and continuously learning emerging AI technologies.

Leads end-to-end machine learning model development, from business problem definition and data exploration through deployment, monitoring, documentation, and retraining. Mentors data scientists, collaborates with stakeholders and product teams, troubleshoots production code and ML pipelines, and guides Agile delivery. Requires advanced expertise in statistical modeling, machine learning, cloud platforms, MLOps, production coding, large datasets, containers, version control, and data lakes.

Leads a data science team focused on customer intelligence and personalization across digital products, apps, newsletters, and marketing channels. Develops lifetime value, segmentation, retention, recommendation, attribution, forecasting, and engagement models. Owns product funnel analytics and experimentation, including A/B testing and causal inference. Partners with Product, Marketing, Editorial, Data Operations, and Engineering to translate customer behavior into product improvements, measurable growth, and scalable data capabilities.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Develop advanced data science and AI/ML solutions using complex healthcare data. Apply statistical modeling, machine learning, NLP, and LLM techniques, including RAG and prompt optimization. Build production-ready workflows integrating models with APIs, data pipelines, enterprise applications, and cloud platforms. Analyze data quality and model performance, communicate findings to technical and executive audiences, and collaborate across product, engineering, clinical, technology, and business teams.

Leads a data science team and personally drives analyses, experimentation, and product analytics to identify patient experience improvements. Partners with Product, Engineering, Research, and Data Engineering teams to develop product features, evaluate business and product performance, guide product planning, and translate statistical findings into actionable recommendations. Manages and develops analytical talent while applying data storytelling, A/B testing, marketplace metrics, and retention, conversion, engagement, and churn analysis.

Leads Lyra Health’s AI/ML engineering roadmap, scales and mentors machine learning teams, and drives production-grade systems for clinical applications. Responsibilities include people leadership, quarterly planning, technical architecture, design reviews, model governance, ML SDLC oversight, and deployment of reliable microservices on Kubernetes. The role partners with product, data science, and clinical stakeholders while ensuring safety, operational excellence, cloud scalability, and compliance for sensitive healthcare data.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Lead end-to-end delivery of strategic data science and AI solutions for AI infrastructure. Work with telemetry and experimentation at fleet scale to build, deploy, and measure predictive and prescriptive models. Partner with stakeholders, present insights, enable adoption, and promote responsible AI. Collaborate closely with data engineers and infrastructure teams to productionize scalable ML solutions.

Lead G2’s experimentation practice across products and digital marketplaces. Design experimentation standards, causal inference methods, metrics, guardrails, and analysis approaches; partner with product and engineering to implement experimentation platforms and instrumentation. Build models, design auctions, ship production systems, evaluate modern statistical methods, and address marketplace interference and low-traffic constraints. Mentor data scientists and evangelize rigorous data science practices across the organization.

Leads Tala’s Machine Learning Engineering team in building and operating platforms for model development, real-time inference, streaming features, batch processing, deployment, monitoring, and production reliability. Manages and develops 4–6 engineers, owns delivery and prioritization, provides architectural leadership, improves engineering practices and SLOs, and partners with Data Science, Data Engineering, Product, Credit, and Business Development teams.