Data and AI Engineer - July 2027

The Brattle Group, a privately held, global economics consulting firm, is looking for a Data and AI Engineer to join our San Francisco, CA office to start in July 2027 .

The Data & AI Engineer is an early-career role within Brattle’s Data & AI Engineering team, focused on hands-on technical work across client delivery, applied R&D, and internal capability-building. This role is designed for candidates who are strong builders, fast learners, and clear communicators, and who want to develop technical judgment in a setting where problems are varied, ambiguous, and often time-sensitive.

This is not a narrowly defined pipeline role or a heads-down engineering track. Data & AI Engineers work across data engineering, applied analytics, machine learning, and AI-enabled workflows, staying close to implementation while learning how technical choices affect real client work.

Candidates are not expected to arrive as experts across data engineering, solution architecture, and applied AI research. They are expected to be hands-on, curious, coachable, and comfortable learning quickly.

Where This Role Sits at Brattle:

The Data & AI Engineering team is a specialized technical group embedded within Brattle’s consulting staff. The team supports client work while also serving as an applied R&D function for the firm: researching emerging technologies, prototyping new analytical and AI-enabled workflows, and translating useful methods into reusable capabilities.

The team partners with economists, consultants, industry experts, and internal stakeholders to improve how the firm works with data, analytics, machine learning, and AI through project delivery, reusable tools, documentation, training, and knowledge-sharing.

Data & AI Engineers work under the guidance of more experienced technical leads, including Senior Data & AI Engineers, Solutions Architects, and Research Engineers. As experience grows, the role can develop toward deeper execution ownership, increased responsibility for solution design, more advanced AI and research work, or some combination of those paths.

Nature of the Work:

Data & AI Engineers work on problems where the technical path is often unclear, the data is imperfect, and the constraints are real. Source materials may be incomplete, inconsistent, degraded through prior systems, or difficult to interpret. Some matters also involve restricted or confidential workflows that shape how data can be accessed, handled, or shared.

The role requires practical judgment. There may be multiple valid approaches, each with trade-offs, and limited ability to go back to the source for clarification. Data & AI Engineers are expected to reason carefully from imperfect inputs, document assumptions, surface limitations, and help build solutions that are defensible, reproducible, timely, and fit for purpose.

Project timelines may shift quickly based on external events, negotiations, litigation deadlines, or client needs. Some work is recurring and operational; some is one-off or exploratory. The right candidate is a resourceful, practical builder: someone who can stay organized, adapt to changing constraints, and make progress without losing rigor or composure.

The team environment is academic, collegial, grounded, and highly collaborative. We value curiosity, humility, initiative, clear communication, and practical intelligence. The work can be technically advanced, but the culture is not ego-driven. We are looking for people who are resourceful, friendly, proactive, flexible, and able to work well with others in a global, connected consulting environment.

Some of the day-to-day responsibilities of this role include:

Prepare, inspect, clean, reconstruct, and validate data from a wide range of sources, including structured datasets, documents, reports, exports, PDFs, scans, and other formats that may not have been created for analysis

Use Python, SQL, notebooks, version control, and related tools to build reproducible workflows, test assumptions, troubleshoot issues, and document how work was performed

Identify data limitations, quality issues, assumptions, blockers, and open questions early

Support applied analytics, machine learning, and AI-enabled workflows where they are 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 to accelerate learning and execution while maintaining responsibility for accuracy, confidentiality, defensibility, and quality

Support applied R&D by helping prototype, test, and evaluate new tools, methods, and workflows before they are adopted more broadly

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

You will not be expected to own full workstreams on day one, but you will be expected to learn quickly, take initiative, and stretch beyond a narrow technical lane.

THE CANDIDATE

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, your individual contribution, the technologies used, and how you evaluated the solution’s performance and limitations.

Comfort working in ambiguous problem spaces where the task may need to be clarified, decomposed, and revised as new information emerges

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, with interest in strengthening applied judgment over time

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 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

Brattle offers a competitive benefits package, base salary, and bonus program for eligible roles base