Senior Manager, Data Science

What Makes Us a Great Place to WorkWe 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 WithYou 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 TeamThe 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 DoModeling and analytical deliveryOwn 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 EngineerDesign, 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 audiencesTranslate 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 onOwn 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 environmentsDesign 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 interpretData and pipeline collaborationWork 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 withDefine 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 outputsContribute 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 themStay 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 choicesInternal stakeholder communication skillsPresent model methodology, outputs, and recommendations directly to senior stakeholders, translating complex statistical concepts into business terms that drive decisionsLead and facilitate working sessions with teams where model assumptions are stress-tested, outputs are interpreted, and methodology is challengedOperate 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 itDrive 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 teamAbout YouTechnicalDeep 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 audiencesStrong 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 trustStrong 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 cleanlyExperience designing and running experiments (A/B testing, out-of-sample validation, back-testing, and sensitivity analysis), and interpreting results with appropriate statistical rigorAbility 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 boundsFamiliarity 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 choicesWorking knowledge of how model outputs are consumed by product interfaces and agentic systems; enough software and product fluency to collaborate effectively with Full-stack Engineers and AI Engineers on how models are integratedNative AI fluency: active use of AI-assisted coding tools to accelerate modeling work, generate and test hypotheses faster, and prototype analytical approaches before committing to full model buildsDemonstrated commitment to responsible analytical practices: honest uncertainty quantification, clear documentation of model limitations, and the judgment to know when a model should not be deployed to a live client engagementExperience8 or more years of applied data science or quantitative modeling experience in a commercial, consulting-adjacent, or product environmentTrack record of building and owning statistical models that real clients or products depend on, not just research outputs, but production-grade analytical frameworks with validation standards and deployment criteriaDeep domain expertise in at least one of: marketing mix modeling and media ROI analytics, brand equity and consumer choice modeling, or pricing elasticity and revenue growth managementDirect experience presenting statistical methodology and model outputs to senior business stakeholders, including C-suite, and defending analytical choices under challenge in a live client or commercial settingExperience working in agile, cross-functional product or case team environments where analytical outputs must survive contact with real-world data and short timelinesExperience collaborating with engineers to translate model outputs into product features or automated workflows is a strong advantageEducationBachelor’s degree in statistics, mathematics, economics, computer science, or a related quantitative field; advanced degree is an advantage but not requiredU.S. Compensation InformationCompensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).Some local governments in the United States require a good-faith, reasonable salary range to be included in job postings for open roles. The estimated annualized compensation for this role is as follows:In Massachusetts, the good-faith, reasonable annualized full-time salary range for this role is between $172,000 – $206,500; placement within this range will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill levelIn Illinois, the good-faith, reasonable annualized full-time salary range for this role is between $164,500 – $197,500; placement within this range will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill levelAnnual discretionary performance bonusThis role may also be eligible for other elements of discretionary compensation4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start dateBain & Company’s comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheckGenerous paid time off, including parental leave, sick leave and paid holidaysFully vested 401(k) company contributionPaid Life and Long-Term Disability insuranceFor all other locations, the good-faith, reasonable annualized full-time salary range for this role is commensurate with competitive geographic market rates for this role and will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level.It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.