Engineer: Data Science

Mayer Brown LLP•Chicago, IL

4d•$124,000 - $164,000•Hybrid

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Data Science or a related field required
2-4 years of hands-on experience delivering data science, machine learning, or AI solutions in production environments
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 architectures, and containerization (Docker/Kubernetes)
Frameworks: Proficiency with modern AI and ML frameworks such as PyTorch, TensorFlow, LangChain, and enterprise AI platforms, such as Azure OpenAI Service
ML Ops practices: Strong understanding of CI/CD, model versioning, monitoring, retraining and lifecycle management using cloud-based tools
Generative AI: Applied experience with large language models (LLMs), embeddings, retrieval-augmented generation (RAG), and building AI agents or chat-based solutions
Data engineering alignment: Familiarity with data integration, ETL, and governance standards to ensure AI/ML solutions align with enterprise architecture
Proven ability to work under pressure and manage multiple priorities, meeting deadlines in a fast-paced environment with shifting priorities
Strong written and verbal communication skills, with the ability to communicate effectively and professionally at all levels of the firm, including senior leadership, technical teams, and external vendors
Ability to work in a diverse, cross-functional team environment, effectively supporting the demanding needs of a global law firm
Must be a self-starter, demonstrating a high level of initiative in problem-solving, process improvement, and driving data management best practices
Strong customer service orientation, anticipating stakeholder needs, proactively addressing concerns, and exercising independent judgment
Exceptional attention to detail and organizational skills, ensuring accuracy in documentation, data integrity, and process adherence across multiple projects
Demonstrates strong strategic thinking and problem-solving skills, analyzing complex data challenges and developing structured, actionable solutions
Ability to present complex data concepts to nontechnical stakeholders, translating technical information into clear, business-friendly insights
Collaborate effectively across departments and organizational levels, ensuring alignment between business needs, IT capabilities, and governance policies
Strong analytical and problem-solving skills with a focus on delivering business value through data-driven solutions to enhance reporting, data governance, and decision making

Nice To Haves

Master’s degree in Computer Science, Data Science or a related field preferred
Law firm or professional services industry experience a plus, but not required
Microsoft Certified: Azure AI Engineer Associate, Azure Data Scientist Associate (preferred)
Databricks Certified or equivalent cloud ML platform certification (preferred)

Responsibilities

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 (LangChain, 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 DevOps preferred).
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