H-1B Visa Head of Data Science Jobs

INTRODUCTION

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ABOUT THE ROLE

Growth AI Performance System is Bain’s integrated suite of agentic decisioning tools giving large organizations always-on intelligence across their most important growth decisions. It sits at a rare intersection few organizations can genuinely offer: the analytical depth and client access of a top-tier consultancy, the technical ambition of a product company, and the commercial stakes of both.

You will lead the statistical and modelling practice behind the platform -- the software that helps Fortune 100 to 500 companies answer where to invest, where to focus, and how to win across marketing ROI, brand strategy, and pricing and commercial analytics. You will work inside Bain’s persistent product team, alongside Tech Leads, Full-stack Engineers, and AI Engineers, with direct feedback from case teams and client engagements flowing back into the models.

The platform has two layers working together: the Agentic Core and three product modules, each solving a distinct commercial problem. The build lifecycle is uniquely connected to the real world. As case teams work alongside clients across industries and geographies, that feedback flows directly back to engineering and gets woven into the product. Every client engagement makes the suite smarter, broader, and more resilient.

This is a startup inside an established company. You get genuine early-stage ownership -- 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 a research-calibre statistician who goes past the first defensible answer when the data and the decision require it -- someone who can derive a model on a whiteboard, implement it correctly in code, validate it rigorously, and defend it in a client room. This is applied statistics in production, at scale, with real users depending on what you build.

WHERE YOU’LL FIT WITHIN THE TEAM

The Head of Data Science owns the statistical and analytical standard for every Growth AI Performance System product module. You sit above module-level Data Scientists, working across blended product and case teams. You are the final word when a modelling question exceeds the standard playbook -- and you are expected to personally lead the hardest, least-precedented problems, not only set direction for others.

You will work across the platform and product modules focused on marketing ROI, brand or pricing strategy. The problems span marketing mix modelling, brand equity measurement, price elasticity, promotional lift, and ranging and assortment -- hard analytical domains with decades of methodology behind them and enormous client-specific noise in front of them. You must know that literature well enough to depart from it when it does not serve the client, and produce outputs credible in both directions: to a methodologically literate peer and to a CMO or Category Director who needs to act.

Analytical outputs here are not research artefacts. They are the core of what the product delivers and what clients trust. The standard you hold the team to is the standard the whole product is measured against.

This role requires a combination that is genuinely rare: research-grade statistical depth, the software discipline to implement models that engineers can maintain, and the client communication skills to present methodology, defend assumptions under live challenge, and adjust the model narrative when the room requires it.

WHAT YOU’LL DO

Analytical leadership and frontier modelling:

• Set and own the statistical standard across all Growth AI Performance System modules: define what methodologically sound means from problem framing through production handoff, and hold every module’s work to that bar
• Personally lead the hardest modelling problems -- where standard Bayesian, time-series, hierarchical, or domain-specific playbooks do not cleanly apply -- including cases requiring custom likelihoods, non-standard priors, partial identification analysis, or cross-model reconciliation
• Take models written as equations -- likelihood, priors, constraints, forward maps -- and implement correct, tested, reproducible estimators in Python using probabilistic programming frameworks; derive estimators from first principles and diagnose identifiability, partial identification, and convergence
• Know when a model cannot support the client question -- not just when library defaults fail silently -- and have the analytical authority to reframe the problem rather than deploy a model that looks fitted but is not identified
• Design and enforce inference and validation standards across all modules: MCMC and VI diagnostics, prior sensitivity, posterior predictive checks, simulation-based calibration, parameter recovery on synthetic ground truth, out-of-sample validation, back-testing, and documented confidence bounds before any output reaches a live client environment
• Own how statistical outputs become structured, versioned data products for engineers and agentic workflows: clear schemas, configuration, interpretable uncertainty measures, and explicit confidence bounds that do not require the original modeller to interpret
• Build and maintain a cross-model reconciliation framework for heterogeneous models that share derived commercial quantities: agreement diagnostics, guarded combination of posterior beliefs, and rules for when reconciliation versus refit is required
• Drive methodological innovation across hierarchical Bayesian methods, causal and quasi-experimental inference, time-series and state-space methods, discrete choice modelling, and regularisation and shrinkage -- bringing improvements into the product when they materially improve client decisions

Domain modelling across product modules:

• Marketing mix and media ROI: ad stock and saturation structures; incrementality calibration via geo experiments, holdouts, or quasi-experimental designs; attribution under collinearity; budget optimization under uncertainty; integration with platform and clean-room data limitations
• Brand equity and consumer choice: latent brand constructs; measurement error models; survey-to-sales linkage; panel representativeness and bias correction; fusion of tracking, behavioral, and sales data
• Pricing, promotional lift, and assortment: promotional lift decomposition; cannibalization and pass-through; elasticity estimation under endogeneity; ranging and assortment under constraints and cross-SKU substitution
• Stay current with domain data sources and their modelling implications -- media platform data, brand tracking and panel data, point-of-sale data, pricing and promotional data -- including coverage gaps, quality limits, and when upstream data issues are driving misleading outputs

Team leadership and stakeholder engagement:

• Build and lead the data science practice: hire, mentor, and develop module-level Data Scientists; set the tone for a team that hunts for the best defensible answer rather than the most convenient one
• Present methodology, outputs, and recommendations directly to senior client stakeholders -- translating complex statistical concepts into decision-relevant terms that include assumptions, failure modes, and what would change the recommendation
• Operate as the practice’s most credible analytical voice under live challenge: defend methodology honestly, acknowledge uncertainty, and adjust the model narrative when the data or the room requires it
• Lead working sessions where assumptions are stress-tested, outputs are interpreted, and methodology is challenged -- including your own; build the kind of client trust that comes from analytical honesty rather than analytical confidence
• Use client feedback as a systematic input to methodological development: identify the modelling problems that are emerging consistently across engagements, and translate those signals into platform improvements that benefit the whole suite

Data and pipeline oversight:

• Work with the Data Engineering and platform Data teams to ensure model inputs are correctly structured, validated, and representative of real commercial data across all three product modules
• Define data requirements standards across modules: schema specifications, quality thresholds, expected ranges, and the edge cases that cause models to produce misleading outputs
• Own evaluation of data quality at the model input layer: diagnose when upstream issues are affecting model outputs, identify root causes, and partner with data teams on resolution

ABOUT YOU

Technical

• PhD in statistics or equivalent quantitative field with dissertation-level applied inference work is desired but not mandatory -- completed or near completion; completed within the last three years strongly preferred
• Research-grade statistical depth: hierarchical and multilevel modelling; Bayesian inference; experimental and quasi-experimental methods; time-series and state-space methods; discrete choice and demand modelling for brand and pricing contexts
• Ability to derive models from first principles and implement them in Python with probabilistic programming frameworks; software discipline to produce code that engineers can maintain, test, and reproduce
• Fluency in inference diagnostics and model criticism: convergence assessment, effective sample size, divergences, posterior predictive checks, cross-validation, prior sensitivity, and pre-specified falsification tests on synthetic data
• Understanding of identifiability, partial identification, and parameter recovery -- including when to stop and reframe the client question rather than deploy a model that looks fitted but is not identified
• Experience designing and running experiments and validation frameworks: A/B tests, geo and holdout designs, out-of-sample checks, back-testing, sensitivity analysis, and calibrated predictive uncertainty that stakeholders can act on
• Ability to translate model outputs into structured, versioned analytical artefacts: mathematical specification, data transforms, inference code, validation notebooks, uncertainty schemas, and deployment criteria
• Familiarity with commercial data sources and their limitations: media platform data, panel data, point-of-sale data, pricing and promotional data
• Working knowledge of how model outputs integrate into product interfaces and agentic workflows: schemas, evaluation harnesses, and explicit uncertainty fields
• Active use of AI-assisted coding tools to accelerate derivation, prototyping, and hypothesis testing -- as a productivity multiplier for rigorous statistical work, not a substitute for inference discipline

Experience

• 3 or more years of applied statistical modelling experience in commercial, consulting-adjacent, or product environments -- PhD, postdoc, internship, and consulting time all count toward this threshold
• Track record of building statistical models that real clients or products depend on -- not only research outputs, but production-grade frameworks with validation standards and deployment criteria
• Direct experience presenting statistical methodology and model outputs to senior business stakeholders and defending analytical choices under live challenge -- including C-suite and equivalent commercial audiences
• Experience working in agile, cross-functional teams where analytical outputs must survive contact with real-world data and decision timelines; demonstrated ability to build defensible first-version models under real timelines, then iterate with validation
• Experience mentoring or managing data scientists, or setting analytical direction for a small practice, is a strong advantage
• Dissertation, publications, or teaching and review experience in applied Bayesian inference, causal inference, econometrics, or marketing and pricing analytics is a strong advantage
• Direct experience with marketing mix modelling, brand measurement, or pricing and promotional elasticity in production or consulting contexts is a strong advantage

Education

PhD in statistics is desired but not mandatory; mathematics, econometrics, or a related quantitative field with a dissertation in applied inference will be considered. We are specifically looking for a research background that brings the instinct to question the established answer and the rigour to prove a better one -- including comfort moving from written formula to estimator to tested code to validated production artefact.

U.S. COMPENSATION INFORMATION

Compensation 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 Illinois, the good-faith, reasonable annualized full-time salary range for this role is between $226,000 - $276,750; placement within this range will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level
• Annual discretionary performance bonus
• This role may also be eligible for other elements of discretionary compensation
• 4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date

Bain & 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 paycheck
• Generous paid time off, including parental leave, sick leave and paid holidays
• Fully vested 401(k) company contribution
• Paid Life and Long-Term Disability insurance

For 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.