Data and AI Engineer - July 2027

The Brattle Group•San Francisco, CA

1d•$100,000 - $126,000•Onsite

About The Position

The Brattle Group is hiring an early-career Data and AI Engineer for its Data & AI Engineering team in San Francisco, California, with a full-time start in July 2027. The team is a specialized technical group embedded in the firm’s consulting staff. It supports client work and conducts applied R&D, researching emerging technologies, prototyping analytical and AI-enabled workflows, and translating useful methods into reusable capabilities. The team collaborates with economists, consultants, industry experts, and internal stakeholders through project delivery, reusable tools, documentation, training, and knowledge-sharing. This hands-on role spans data engineering, applied analytics, machine learning, and AI-enabled workflows. Work may involve unclear technical paths, imperfect or difficult-to-interpret data, restricted or confidential workflows, and shifting timelines. Engineers are expected to make practical, careful judgments; document assumptions and limitations; and help produce defensible, reproducible, timely, fit-for-purpose solutions. Work may be recurring, operational, one-off, or exploratory. The position is intended for a curious, coachable, resourceful builder who communicates clearly and learns quickly. The engineer works under experienced technical leads, including Senior Data & AI Engineers, Solutions Architects, and Research Engineers. Over time, the role may develop toward deeper execution ownership, solution design, advanced AI and research work, or a combination of these areas. The successful candidate will not be expected to own full workstreams on day one, but will be expected to take initiative and develop beyond a narrow technical lane. The anticipated base gross salary range in San Francisco is $100,000–$126,000 annually. Actual salary depends on factors including experience and training. The role is not eligible for immigration sponsorship.

Requirements

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, and ability to explain the problem, personal contribution, technologies used, and evaluation of performance and limitations.
Comfort working in ambiguous problem spaces where tasks may need clarification, decomposition, and revision as information emerges.
Strong foundation in Python for data analysis, scripting, automation, or prototyping, with exposure to 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, with interest in strengthening applied judgment.
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.
Ability to learn new tools quickly and use AI-assisted development responsibly without assuming generated output is correct.
Strong written and verbal communication skills, including ability to explain technical work, assumptions, limitations, and next steps clearly.
Ability to manage multiple parallel workstreams in a fast-paced environment.
Flexible mindset to adapt to changing project priorities and client needs.

Nice To Haves

Familiarity with cloud platforms such as Azure or comparable environments; helpful but not required.

Responsibilities

Prepare, inspect, clean, reconstruct, and validate data from structured datasets, documents, reports, exports, PDFs, scans, and other sources.
Use Python, SQL, notebooks, version control, and related tools to build reproducible workflows, test assumptions, troubleshoot issues, and document work performed.
Identify data limitations, quality issues, assumptions, blockers, and open questions early.
Support applied analytics, machine learning, and AI-enabled workflows when useful to the problem.
Contribute to workflows involving text extraction, classification, summarization, embeddings, retrieval-augmented generation, model evaluation, automation, visualization, or rapid prototyping.
Use AI tools thoughtfully while maintaining responsibility for accuracy, confidentiality, defensibility, and quality.
Help prototype, test, and evaluate new tools, methods, and workflows through applied R&D.
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.
Turn lessons from project work and applied R&D into reusable team assets, examples, templates, documentation, and training materials.