Senior Manager, Data Science

Boston, Chicago, London, Sao Paulo, Warsaw

Description & Requirements What Makes Us a Great Place to Work

We are proud to be consistently recognized as one of the worlds best places to work. We are currently the top ranked consulting firm on Glassdoor's Best Places to Work list and have earned the 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 will be part of a team building Bain's integrated suite of agentic decisioning tools that gives large organizations always-on intelligence across their most important growth decisions. The role sits at an intersection few organizations can offer: the analytical depth and client access of a top-tier consultancy, the technical ambition of a product company, and engineering work at the frontier of applied AI.

You will work inside Bain's standing product team, building software that helps the world's largest companies answer the questions that drive their business: where to invest, where to focus, and how to win. The impact of what you build is direct and immediate, and the platform compounds in value with every release.

The platform has two layers working together: an agentic core and a set of decision modules, each solving a distinct commercial problem. The build lifecycle is connected to the real world: as case teams work alongside clients across industries and geographies, that feedback flows straight back to engineering and into the product. Every client engagement makes the platform smarter, broader, and more resilient.

This is a startup inside an established company. You get genuine early-stage ownership and real influence over architecture and product direction, backed by Bain's global scale, client relationships, and leadership commitment; the pace of a startup with the runway of a global firm.

We are looking for data scientists who see how to make a product module smarter and bring that instinct to every sprint without being asked, and who are comfortable with problems that do not have a known answer. The work involves Bayesian statistical modeling, experiment design, and data problems with no off-the-shelf answer. Experience with agentic systems matters: understanding how model outputs feed into agentic workflows and evaluation harnesses, and entity resolution across messy enterprise data. This is applied AI in production, at scale, with real users depending on what you build. The field is moving quickly and the team is built to move with it, so the willingness to keep learning counts for as much as what you already know.

Where You'll Fit Within the Team

The Data Scientist owns the statistical and analytical core of a Growth AI Performance System product module, designing, building, and validating the models that power client-facing outputs across marketing ROI, brand strategy, or pricing and commercial analytics. You will work within a blended product and case team, sitting under the Tech Lead and working alongside the Full-stack Engineer to translate model outputs into scalable, repeatable product features that consulting teams and their clients depend on in live engagements.

You will work across the platform and product modules focused on marketing ROI, brand or pricing strategy, translating the commercial and analytical questions that clients are trying to answer into models that are methodologically sound, production-ready, and interpretable enough for senior client stakeholders to understand, challenge, and act on. The analytical outputs you produce are not research artifacts: they are the core of what the product delivers, and what clients trust.

This role sits at the intersection of applied statistics, product thinking, and delivery. The problems you will work on (marketing mix modeling, brand equity measurement, price elasticity, promotional lift) are hard analytical problems with decades of methodology behind them and an enormous amount of client-specific noise in front of them. The job is to produce outputs that are credible in both directions: to a methodologically literate peer and to a CMO or Category Director who needs to make a decision.

The build lifecycle here is uniquely connected to real commercial outcomes. Feedback from client engagements flows directly back into the models, and every engagement makes the analytical framework smarter, more robust, and more reusable across the suite. You will engage directly with consulting teams and senior stakeholders, presenting methodology, defending assumptions, and adjusting models based on what you learn in the room.

What You'll Do

Modeling and analytical delivery

• Own the statistical modeling and analytical delivery for your product module workstream (from problem framing, hypothesis development, and data preparation through model build, validation, and production handoff), working under the direction of the Tech Lead and in close partnership with the Full-stack Engineer
• Design, build, and validate Bayesian statistical models, time-series forecasts, and domain-specific analytical frameworks (marketing mix models, brand equity models, price elasticity and promotional lift models) that are methodologically sound, production-ready, and defensible to senior audiences
• Translate statistical model outputs into structured, versioned data products that the Full-stack Engineer and AI Engineering team can consume for product interfaces and agentic workflows; design outputs with interpretable confidence and uncertainty measures that professional users can act on
• Own the validation and evaluation framework for your workstream: back-testing, out-of-sample checks, sensitivity analysis, and documented confidence bounds; define and enforce deployment-readiness standards consistently before model outputs reach live client environments
• Design models and outputs so they can be operated by the broader team and, over time, by automated agentic workflows: clear configuration, interpretable outputs, and explicit uncertainty measures that do not require the original modeler to interpret

Data and pipeline collaboration

• Work closely with the Data Engineer and platform Data team to ensure the data flowing into your models is correctly structured, validated, and representative of the real-world commercial data the module is designed to work with
• Define the data requirements for your models clearly enough that data engineers can build pipelines to meet them: schema specifications, quality thresholds, expected ranges, and the edge cases that cause models to produce misleading outputs
• Contribute to the evaluation framework for data quality at the model input layer: identify when upstream data issues are affecting model outputs, diagnose root causes, and work with the data team to resolve them
• Stay current with domain-specific data sources and benchmarks relevant to your module (media spend and attribution data, brand tracking and panel data, pricing and promotional data), and understand how their structure and limitations affect modeling choices
• Present model methodology, outputs, and recommendations directly to senior stakeholders, translating complex statistical concepts into business terms that drive decisions
• Lead and facilitate working sessions with teams where model assumptions are stress-tested, outputs are interpreted, and methodology is challenged
• Operate as a credible, composed analytical voice in stakeholder settings: able to defend methodology under challenge, acknowledge uncertainty honestly, and adjust the model narrative when the data or the room requires it
• Drive innovation in analytical methodology: stay current with advances in Bayesian inference, marketing mix modeling, brand measurement, and commercial analytics; bring relevant methodological improvements into the product and share learnings with the broader data science team

About You

Technical

• Deep Bayesian statistical expertise: able to design prior structures, diagnose convergence issues, stress-test assumptions, and defend inferences to both methodologically literate peers and non-technical senior audiences
• Strong domain knowledge in at least one product module area: marketing mix modeling and media ROI measurement, brand equity and consumer choice modeling, or pricing and revenue growth management analytics: with the methodological depth to produce outputs that clients trust
• Strong Python proficiency for statistical modeling and data analysis: probabilistic programming, scientific computing, and the software engineering discipline to produce model code that can be handed off to engineers cleanly
• Experience designing and running experiments (A/B testing, out-of-sample validation, back-testing, and sensitivity analysis), and interpreting results with appropriate statistical rigor
• Ability to translate statistical model outputs into structured data products that engineers can consume and agentic workflows can operate: clear schemas, documented uncertainty measures, and explicit confidence bounds
• Familiarity with the data sources and benchmarks relevant to commercial analytics: media platform data, panel data, point-of-sale data, pricing and promotional data, and the quality and coverage limitations that affect modeling choices
. click apply for full job details