Data and AI Engineer

Summary

The Brattle Group, a privately held, global economics consulting firm, is looking for a Data and AI Engineer to join our Boston, MA office. This early-career role focuses on hands-on technical work across client delivery, applied R&D, and internal capability-building, while supporting data engineering, applied analytics, machine learning, and AI-enabled workflows.

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 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

Skills

• 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
• Demonstrated interest in using AI tools, machine learning methods, or automation to solve practical problems, with willingness to learn how to evaluate those tools responsibly
• 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
• 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
• Familiarity with cloud platforms such as Azure or comparable environments is helpful but not required

Qualifications

Must Haves

• 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
• Demonstrated interest in using AI tools, machine learning methods, or automation to solve practical problems, with willingness to learn how to evaluate those tools responsibly
• 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
• 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 is helpful but not required

Benefits

• Brattle offers a competitive benefits package, base salary, and bonus program for eligible roles based on individual and firm performance.