Data and AI Engineer

About the Role

The Brattle Group seeks a Data and AI Engineer for its San Francisco, CA office, starting in July 2027. This is an early-career role focused on hands-on technical work across client delivery, applied R&D, and internal capability-building. The role is designed for individuals who are strong builders, fast learners, clear communicators, and are eager to develop technical judgment in a dynamic environment.

Data & AI Engineers work across data engineering, applied analytics, machine learning, and AI-enabled workflows, staying close to implementation and understanding how technical choices impact client work. Candidates are expected to be hands-on, curious, coachable, and comfortable learning quickly, rather than arriving as experts in all areas.

The Data & AI Engineering team is a specialized technical group embedded within Brattle’s consulting staff, supporting client work and acting as an applied R&D function by researching emerging technologies and prototyping new workflows. The team partners with economists, consultants, and internal stakeholders to enhance data, analytics, machine learning, and AI capabilities through project delivery, reusable tools, documentation, training, and knowledge-sharing.

Key Responsibilities

• Prepare, inspect, clean, reconstruct, and validate data from a wide range of sources, including structured datasets, documents, reports, exports, PDFs, scans, and other formats.
• Use Python, SQL, notebooks, version control, and related tools to build reproducible workflows, test assumptions, troubleshoot issues, and document work.
• Identify data limitations, quality issues, assumptions, blockers, and open questions early.
• Support applied analytics, machine learning, and AI-enabled workflows.
• Contribute to workflows involving text extraction, classification, summarization, embeddings, retrieval-augmented generation, model evaluation, automation, visualization, or rapid prototyping.
• Use AI tools thoughtfully to accelerate learning and execution while maintaining responsibility for accuracy, confidentiality, defensibility, and quality.
• Support applied R&D by prototyping, testing, and evaluating new tools, methods, and workflows.
• Communicate progress, technical findings, assumptions, limitations, and trade-offs clearly to consultants, economists, technical peers, and other stakeholders.
• Participate in code review, collaborative problem solving, documentation, and iterative refinement of work products.
• Help turn lessons from project work and applied R&D into reusable team assets, examples, templates, documentation, and training materials.

Requirements & Tech Stack

• Bachelor’s degree in Computer Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, Economics with strong technical coursework, or a related field.
• Equivalent hands-on technical experience, internships, research work, or project-based experience may also be considered.
• 0-3 years of professional experience in data engineering, analytics, applied AI, machine learning, software development, research, or related technical work.
• Must be available to begin full-time employment in July 2027.
• Hands-on experience building at least one working AI-enabled application, tool, or automated workflow through coursework, research, an internship, a capstone, or an independent project.
• Ability to explain the problem addressed, individual contribution, technologies used, and how the solution’s performance and limitations were evaluated.
• Comfort working in ambiguous problem spaces where tasks may need clarification, decomposition, and revision.
• Strong foundation in Python for data analysis, scripting, automation, or prototyping, with exposure to libraries such as pandas, NumPy, scikit-learn, or comparable tools.
• Working knowledge of SQL and relational data concepts, including joins, aggregation, filtering, and practical data exploration.
• Foundational understanding of statistics, data analysis, machine learning, or experimental evaluation.
• Exposure to generative AI workflows, such as prompt design, embeddings, vector search, retrieval-augmented generation, summarization, classification, or model evaluation.
• Ability to work with structured, semi-structured, and unstructured data, including text-heavy documents or heterogeneous data sources.
• Familiarity with software development practices such as Git, notebooks, code review, documentation, testing, and reproducible workflows.
• Familiarity with cloud platforms such as Azure or comparable environments is helpful but not required.
• Ability to learn new tools quickly and use AI-assisted development responsibly.
• Strong written and verbal communication skills.
• Ability to manage multiple parallel workstreams in a fast-paced environment.
• Flexible mindset to adapt to changing project priorities and client needs.