Data and AI Engineer

Boston-based, onsite early-career role focused on hands-on data engineering and applied AI-enabled workflows that support client delivery and applied R&D. The Brattle Group’s Data & AI Engineering team works in an academic, collegial, and highly collaborative environment, partnering with economists, consultants, industry experts, and internal stakeholders to turn messy, imperfect inputs into defensible, reproducible, and fit-for-purpose solutions.

How you’ll contribute

• Prepare, inspect, clean, reconstruct, and validate data from many sources, including structured datasets and text-heavy or heterogeneous inputs such as documents, reports, exports, PDFs, scans, and other formats not originally created for analysis.
• Build reproducible workflows using Python, SQL, notebooks, version control, and related tools, including testing assumptions, troubleshooting issues, and documenting how results were produced.
• Surface data limitations, quality issues, assumptions, blockers, and open questions early to keep work on track and transparent.
• Support applied analytics, machine learning, and AI-enabled workflows when they help solve the problem.
• Contribute to workflows such as 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.
• Assist applied R&D by prototyping, testing, and evaluating new tools, methods, and workflows before broader adoption.
• 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 deliverables.
• Turn lessons from project work and applied R&D into reusable team assets, including examples, templates, documentation, and training materials.

What you bring

• 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.
• Interest in using AI tools, machine learning methods, or automation to solve practical problems, with a willingness to learn how to evaluate tools responsibly.
• Comfort working in ambiguous problem spaces where tasks may need to be clarified, decomposed, and revised as new information emerges.
• Strong foundation in Python for 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, including 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 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 explaining 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.

Tools and technologies

• Python, SQL, notebooks, version control
• pandas, NumPy, scikit-learn, Git
• embeddings, retrieval-augmented generation, vector search
• prompt design, summarization, classification, model evaluation
• Azure

Compensation and benefits

• Competitive benefits package, base salary, and a bonus program for eligible roles based on individual and firm performance.
• Anticipated base gross salary range in Boston, MA: $105,000 - $115,000 annually.
• Actual salary depends on factors including experience and training.

Where this role sits at Brattle

• The Data & AI Engineering team is embedded within Brattle’s consulting staff and serves both client delivery and applied R&D.
• The team researches emerging technologies, prototypes new analytical and AI-enabled workflows, and translates useful methods into reusable capabilities.
• Engineers work under the guidance of more experienced technical leads, including Senior Data & AI Engineers, Solutions Architects, and Research Engineers.

What the work feels like

• Technical paths are often unclear, data is imperfect, and constraints are real, including some restricted or confidential workflows.
• Multiple approaches may be valid, each with trade-offs and limited ability to revisit original sources.
• Timelines may shift quickly based on external events, negotiations, litigation deadlines, or client needs.
• Work can be recurring and operational or one-off and exploratory.

Day-to-day expectations: you won’t 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.