Engineer: Data Science

Overview

Mayer Brown is an international law firm positioned to represent the world’s major corporations, funds, and financial institutions in their most important and complex transactions and disputes. We are recognized by our clients as strategic partners with deep commercial instincts and a commitment to creatively anticipating their needs and delivering excellence in everything we do.

We are a collegial, collaborative firm where highly motivated individuals with an unwavering commitment to excellence receive the opportunity, support, and development they need to grow, thrive, and realize their greatest potential all while supporting the Firm’s client service principles of excellence, strategic partnership, commercial instinct, integrated strengths, innovation, and collaboration across our international firm. If you enjoy working with team members whose defining characteristics are exceptional client service, initiative, professionalism, responsiveness, and adaptability, you may be the person we are seeking to join our Information Technology department in our Chicago office, as an Engineer:
Data Science.

The Engineer:
Data Science is responsible for the design, development, and delivery of advanced analytics and AI solutions in support of the firm’s Data and AI strategy. This role works closely with the data science team, IT engineers, and business teams to implement reliable, scalable solutions that deliver measurable business value.

The Engineer applies experience in data science, AI methods, and modern engineering practices to build and deploy solutions in production environments. The role emphasizes delivery excellence – ensuring that solutions are practical, efficient, and compliant with the firm’s standards for security, confidentiality, and governance. Working closely with data science, IT, and data teams, the Engineer translates complex concepts into practical solutions that support critical business outcomes.

Essential Functions:

• Solution Delivery
  • Design, build, and deploy data science and AI solutions end-to-end, from design and development through testing, release, monitoring, and support.
  • Operationalize models with CI/CD pipelines, automated testing, and monitoring, applying MLOps practices such as versioning, retraining, and drift detection (tools: MLflow, Azure ML, Databricks)
  • Leverage both open-source frameworks (Lang Chain, Hugging Face, etc.) and enterprise platforms (Azure OpenAI, Databricks, etc.) to deliver production ready, scalable AI solutions
  • Implement generative AI and advanced analytics features, including embeddings, retrieval-augmented generation, and building AI agents and chat-based solutions.
  • Write clean, testable, and well-documented code using modern engineering practices (unit testing, code reviews, API development, Azure Dev Ops preferred).
• Technical Design & Architecture
  • Ensure solutions align with enterprise architecture, data governance, and security standards
  • Collaborate with enterprise architects, IT, and business stakeholders to validate approaches
  • Contribute to lifecycle management practices including model versioning, monitoring, and continuous improvement of delivery processes
  • Evaluate and pilot emerging technologies to improve scalability and solution quality

Education/Training/

Certifications:

• Bachelor’s degree in Computer Science, Data Science or a related field required
• Master’s degree in Computer Science, Data Science or a related field preferred

Professional

Experience:

• 2-4 years of hands‑on experience delivering data science, machine learning, or AI solutions in production environments
• Law firm or professional services industry experience a plus, but not required

Certifications

• Microsoft Certified:
  Azure AI Engineer Associate, Azure Data Scientist Associate (preferred)
• Databricks Certified or equivalent cloud ML platform certification (preferred)

Technical

Skills:

• AI/ML solution delivery:
  Experience developing, testing, and deploying complex, high-impact solutions into production, ensuring reliability and scalability
• Cloud Platforms:
  Hands‑on with Azure (preferred), AWS, or GCP; familiarity with Microsoft Fabric/Synapse, data lakehouse…