Associate, Full-Stack Data Scientist

WHAT MAKES US A GREAT PLACE TO WORK

We are proud to be consistently recognized as one of the world's best places to work. We are currently the top-ranked consulting firm on Glassdoor's Best Places to Work list and have earned the #1 overall spot a record seven times. Extraordinary teams are at the heart of our business strategy, but these don't happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.

WHO YOU'LL WORK WITH

You'll join our Data Science & Machine Learning Engineering experts within the Applied Technology & AI team. This team is part of Bain's digital capabilities practice, which includes experts in analytics, engineering, product management, and design. In this multidisciplinary environment, you'll leverage deep technical expertise with business acumen to help clients tackle their most transformative challenges. You'll work on integrated teams alongside our general consultants and clients to develop data-driven strategies and innovative solutions. Together, we create data science solutions that harness the power of data and artificial intelligence to drive competitive advantage for our clients. Our collaborative and supportive work environment fosters creativity and continuous learning, enabling us to consistently deliver exceptional results.

THE ROLE

As an Associate, Full-Stack Data Scientist, you will design, implement and refine models that turn multi-modal data into production solutions across a wide range of industries and business problems. You bring a generalist foundation across core data science methods (e.g., machine learning, forecasting, statistics, and optimization) that lets you move fluidly across problem types. You’ll develop and deploy analytical and machine learning models that power production systems such as dynamic personalization and pricing engines, predictive maintenance and always-on forecasting and optimization capabilities. 

You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings.

Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time.

WHAT YOU’LL DO

• Build, test, and refine machine learning and analytical models that power production systems across a range of problem types, such as pricing and personalization engines, demand forecasting, and predictive maintenance, taking them from exploration and feature engineering through validation and deployment
• Work with client data engineering and analytics teams to acquire, extract, and prepare data for modeling at scale, including cleaning, enriching, de-duping, and joining structured, semi-structured, and unstructured data
• Partner with consulting teams to translate client business problems into analytical approaches and help scope the work
• Use AI tools with guardrails to accelerate your work, from exploration and prototyping through implementation
• Apply generative AI where it offers a better path to a client solution, using techniques such as prompt engineering and retrieval-augmented generation
• Write production-quality code within Bain's model development life cycle, including documentation, unit/integration testing, and code reviews
• Work with machine learning engineering (MLE) teams to deploy and operationalize models so they run reliably in production
• Communicate results clearly to technical and non-technical audiences, contribute to working sessions and presentations, and write clear technical documentation
• Contribute to reusable code, models, and best practices across the team
• Stay current with emerging data science and AI techniques and share what you learn with the team

ABOUT YOU

Required:

• A generalist background across core data science methods, with strong modern machine learning skills (e.g., gradient-boosted models, deep learning with PyTorch or similar) and a solid grounding in statistical modeling (including causal inference and Bayesian methods) and optimization
• An interest in applying your expertise across a wide array of industries and problems
• Proficiency in common data science programming languages (Python required) and working knowledge of SQL
• Solid software engineering practices (version control, testing, code review) and familiarity with CI/CD
• Hands-on experience (through work, research, or projects) with at least one of the following problem types: 
  • Marketing mix modeling (Bayesian regression, causal inference, adstock/saturation modeling)
  • Price elasticity and optimization (econometrics, causal inference, optimization)
  • Segmentation and personalization (clustering, embedding-based recommendation systems, experimentation, bandits)
  • Supply chain / OR (optimization, simulation)
  • Demand forecasting (time series, hierarchical forecasting, deep learning and foundation models)
  • Predictive maintenance (survival analysis, anomaly detection, sequence models on sensor data)
• Experience building and evaluating LLM-based applications or prototypes (e.g., prompt engineering, retrieval-augmented generation)
• Experience in AI-native delivery, such as using AI-assisted coding tools to accelerate modeling work, generate and test hypotheses, and prototype analytical approaches
• Familiarity with how models are deployed and maintained in production
• Experience working in agile, cross-functional team environments 
• Strong interpersonal and communication skills, including the ability to discuss technicalities of solutions, algorithms, and techniques with colleagues and clients
• Curiosity, proactivity, and critical thinking
• Ability to collaborate with people at all levels and with multi-office/region teams
• Ability to operate in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a collaborative team
• Advanced degree in Data Science, Computer Science, Mathematics, Engineering, Econometrics, Statistics or Information Sciences
• At least 1 year of experience in advanced analytics or related field

Preferred (non-requisite)

• Hands-on experience with two or more of the problem types above
• Experience taking models into production with MLE teams
• Experience with transformer-based and foundation models for text, sequence, or time-series data, including embeddings and fine-tuning
• Experience applying Bayesian or causal inference methods (e.g., for marketing mix modeling or pricing) 
• Experience deploying ML models using containerization (Docker, Kubernetes), infrastructure-as-code (Terraform), and MLOps tooling such as MLflow or Kubeflow, and GitHub Actions
• Experience managing data workflows and pipelines for modeling 
• Experience scaling model training and inference on large datasets, and optimizing pipelines for performance in production environments
• Experience building end-to-end prototypes or tools (e.g., web apps, dashboards) using AI coding assistants
• Experience building agentic harnesses and orchestration of agentic processes
• Experience with relational or NoSQL databases (e.g., PostgreSQL, SQL Server, Oracle, MySQL, Redis, MongoDB, Elasticsearch, Snowflake, or similar)
• PhD is a plus
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This role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.

Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.