Data and AI Engineer - July 2027

The Brattle Group•San Francisco, CA

10h•$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 July 2027 start. The team is a specialized technical group embedded within the firm’s consulting staff. It supports client work and conducts applied research and development, including researching emerging technologies, prototyping analytical and AI-enabled workflows, and turning useful methods into reusable capabilities. The role involves hands-on work across data engineering, applied analytics, machine learning, and AI-enabled workflows. Engineers collaborate with economists, consultants, industry experts, technical peers, and internal stakeholders. Under the guidance of experienced technical leads, including Senior Data & AI Engineers, Solutions Architects, and Research Engineers, the engineer develops technical judgment and may progress toward greater execution ownership, solution design, or advanced AI and research work. Work often involves unclear technical paths, imperfect or difficult-to-interpret source data, and practical constraints such as restricted or confidential workflows. The engineer is expected to make careful decisions, document assumptions and limitations, and help produce defensible, reproducible, timely, fit-for-purpose work. Project priorities and timelines can change in response to external events, negotiations, litigation deadlines, or client needs. The team describes its environment as academic, collegial, grounded, and collaborative, valuing curiosity, humility, initiative, clear communication, and practical intelligence. This is an early-career role for a hands-on, adaptable builder. The successful candidate will learn quickly, take initiative, communicate clearly, and work across multiple technical areas rather than focusing on a narrowly defined engineering lane. A bachelor’s degree in a relevant technical or quantitative field is requested, though equivalent hands-on experience may also be considered. Applicants should have 0–3 years of relevant professional experience and a working AI-enabled application, tool, or automated workflow they can explain and discuss. The anticipated base gross salary range for San Francisco is $100,000–$126,000 annually. Actual salary depends on factors including experience and training. The position is not eligible for immigration sponsorship. The Brattle Group is an Equal Opportunity Employer. The firm has 500 professionals across North America, Europe, and Asia-Pacific.

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, or project 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 as new 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, and 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 treating generated output as automatically correct.
Strong written and verbal communication skills, including the 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.
Identify data limitations, quality issues, assumptions, blockers, and open questions early.
Support applied analytics, machine learning, and AI-enabled workflows where appropriate.
Contribute to text extraction, classification, summarization, embeddings, retrieval-augmented generation, model evaluation, automation, visualization, and rapid prototyping workflows.
Use AI tools thoughtfully while maintaining responsibility for accuracy, confidentiality, defensibility, and quality.
Support applied R&D by prototyping, testing, and evaluating tools, methods, and workflows before broader adoption.
Communicate progress, findings, assumptions, limitations, and trade-offs 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.
Learn quickly, take initiative, and extend beyond a narrow technical lane.