Senior Data Engineer (Applied ML / Analytics)

The Brattle Group, a privately held, global economics consulting firm, is looking for a Senior Data Engineer (Applied AI/ML) to join our Boston, MA office. The Senior Data Engineer at Brattle has a dual role spanning both client-facing project work and substantial internal research and development. The role sits at the intersection of data engineering, applied machine learning, and analytical consulting. Senior Data Engineers translate complex client questions and messy real-world data into defensible analytical approaches, exercising judgment about when classical statistical methods, machine learning techniques, or newer approaches such as generative AI are appropriate.

On client engagements, Senior Data Engineers define technical approaches, lead analytical workstreams, and make key implementation decisions while collaborating closely with Principals, consultants, and subject-matter experts. They evaluate modeling strategies, articulate technical trade-offs, and guide teams toward practical solutions that hold up under real-world data and time constraints.

Beyond project delivery, the role contributes significantly to internal research and development focused on expanding the team’s analytical capabilities. This includes researching and prototyping new methods, tools, and workflows across structured and unstructured data, including text analysis, vector-based methods, and LLM-enabled approaches, and helping transition promising prototypes into approaches that can be applied in client work.

The role emphasizes modeling judgment, feature design, and evaluation rather than routine pipeline construction, building on Brattle’s existing work in Python-based machine learning, unstructured data analysis, and generative AI workflows.

 

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

• Define and lead technical workstreams for client and internal projects, including selecting analytical approaches, evaluating modeling strategies, and determining appropriate tools and methods for the problem context
• Triage and assess incoming client data from a wide range of sources and maturity levels, determining data readiness, required normalization, and appropriate analytical paths
• Exercise independent judgment in ambiguous situations, evaluating competing analytical approaches and technical trade-offs, and articulating clear recommendations to project teams, Principals, and clients
• Translate complex client questions and messy real-world data into appropriate analytical methods, determining when classical statistical methods, machine learning techniques, generative AI workflows, or simpler approaches are warranted
• Research, prototype, and evaluate new analytical methods, tools, or workflows that expand the team’s capabilities, particularly in areas such as text analysis, unstructured data, vector-based methods, and LLM-enabled workflows
• Develop proof-of-concept analyses and models, and structure handoff plans for junior engineers to implement, refine, and scale where appropriate
• Review code submitted by engineers, providing technical feedback and guidance on design, modeling choices, and maintainability
• Write reproducible Python-based analyses for data processing, unstructured data exploration, modeling, and evaluation
• Contribute to internal documentation, training, and knowledge sharing, helping establish recommended practices and reusable analytical approaches
• Coordinate with IT as needed on software licensing, tooling evaluation, and deployment of new analytical technologies

 

THE CANDIDATE

• Bachelor’s Degree in Computer Science, Computer Engineering, or related field; advanced degree is a plus
• 4–7 years of professional experience in data engineering, analytics, or related technical roles
• Proven track record delivering complex, ambiguous data projects, including selecting approaches and managing trade-offs
• Demonstrated ability to research and pilot new technologies or methodologies
• Strong proficiency in Python for data processing, automation, exploratory analysis, and modeling
• Applied machine learning experience, including evaluating and contrasting modeling approaches (e.g., classification, ranking, clustering, vector-based methods, and generative AI techniques) based on problem context, data quality, and practical constraints
• Experience wit