Innovation AI/ML Data Scientist in Chicago

Job DescriptionJob Description

Location: Chicago, IL

Our client is a distinguished global provider of legal services. This is a full-time, direct hire onsite role. We are seeking an accomplished Innovation AI/ML Data Scientist to architect and implement advanced, data-driven solutions that improve quality, efficiency, and decision-making throughout the firm's legal services and operations.

In close collaboration with attorneys, innovation leaders, and cross-functional enterprise teams, you will translate complex legal and organizational challenges into secure, scalable artificial intelligence and machine learning solutions. You will oversee the entire solution lifecycle—from experimentation and performance measurement through deployment and ongoing monitoring. Responsibilities include curating and governing datasets, establishing successful criteria for accuracy and safety, and developing production-ready models and applications, including both large model (LLM)-based and traditional machine learning approaches.

Reporting to the Director of Innovation Engineering, you will play a pivotal role in advancing and expanding the firm’s data science initiatives—streamlining routine processes, producing actionable insights, and enabling new capabilities that enhance client outcomes and organizational efficacy.

The firm maintains rigorous standards for performance and cultivates a growth-oriented culture. Individuals who demonstrate initiative and commitment will have opportunities for expanded responsibilities and long-term career growth within the organization.

Must be a U.S. or Green Card holder – We cannot accept work visas or sponsorship at this time

Desired Qualifications and Experience:

· Bachelor’s degree in computer science, statistics, mathematics, engineering, or a related quantitative discipline required; preference given to candidates with advanced degrees.

· Minimum of seven years delivering AI/ML solutions within professional services, financial, legal, or technical sectors, featuring significant cross-functional collaboration among legal and business stakeholders.

· Proficient in Python for data analysis, statistical modeling, and AI/ML prototyping (including pandas, PyTorch, TensorFlow); skilled in advanced SQL; experienced in constructing data pipelines and preparing both structured and unstructured datasets.

· AI & LLM Application Knowledge: Practical experience with AI platforms and tools, such as OpenAI, Microsoft Azure OpenAI, Hugging Face, LangFlow, and LangChain; strong understanding of vector databases, retrieval methods for unstructured data, and knowledge graph–based applications.

· Demonstrated ability to design and implement evaluation frameworks for enterprise AI solutions, including monitoring accuracy, robustness, hallucinations, bias, and fairness.

· Experienced with data visualization tools including Plotly, Tableau, and Microsoft Power BI; capable of translating complex findings into actionable insights.

· Familiarity with legal use cases such as due diligence, contract review, and legal research; prior experience in a law firm or Microsoft Azure environment is .

· Proven track record mentoring colleagues, collaborating across multidisciplinary teams, and promoting widespread adoption of AI solutions.

Summary of Responsibilities:

· Translate Legal Workflows into AI Solutions – Facilitate discovery sessions with attorneys and practice stakeholders to systematically map end-to-end legal processes into actionable data and LLM requirements, along with measurable success criteria and evaluation plans aligned with business objectives.

· Design and Prototype Intelligent Applications – Architect and assess AI/ML solutions, including retrieval-augmented (RAG) pipelines, vector database integrations, and orchestration frameworks, to determine feasibility, optimize performance, and ensure scalability.

· Deliver High-Impact Legal Use Cases – Develop robust solutions addressing contract analysis, due diligence review, document summarization, large-scale agreement comparisons, and other practice-driven requirements.

· Establish Evaluation & Governance Frameworks – Define and implement rigorous production-ready evaluation standards, focusing on accuracy, robustness, bias and fairness, hallucination mitigation, and business-aligned performance benchmarks, with ongoing monitoring protocols.

· Develop Reusable Assets & Libraries – Create comprehensive internal libraries, documentation, and standardized development practices to promote efficient and scalable delivery of AI solutions.

· Collaborate Across Technical & Legal Teams – Engage closely with embedded data science teams and business stakeholders to align priorities, co-create solutions, and drive successful adoption across practice groups.

· Communicate Insights with Clarity – Present evaluation results, trade-offs, risks, and projected outcomes in clear, accessible terms for non-technical stakeholders, including attorne