Head of Data Science

Bain & Company

3d • $226,000 - $276,750

About The Position

The Head of Data Science owns the statistical and analytical standards for Bain’s Growth AI Performance System, an integrated suite of agentic decisioning tools that supports commercial decisions in marketing ROI, brand strategy, pricing, and commercial analytics. The role leads the statistical and modelling practice across product modules and works with product and case teams, including Tech Leads, Full-stack Engineers, AI Engineers, and Data Engineering and platform Data teams. Client feedback from engagements informs ongoing model and product development.

This is a senior, hands-on leadership role: the Head of Data Science sets methodological direction while personally leading the most difficult and unprecedented modelling problems. The work spans hierarchical Bayesian modelling, causal and quasi-experimental inference, time-series and state-space methods, discrete choice modelling, and regularisation and shrinkage. The role requires translating mathematical specifications into tested, reproducible Python estimators and production-ready analytical outputs, establishing rigorous validation and inference standards, and ensuring models can support the decisions clients need to make.

The role also builds and leads the data science practice, develops module-level Data Scientists, oversees model-input data quality and requirements, and presents and defends methodologies and recommendations to senior client stakeholders. The position is permanent and full-time, with locations listed in Chicago, London, Sao Paulo, and Warsaw.

Requirements

• Research-grade statistical depth in hierarchical and multilevel modelling, Bayesian inference, experimental and quasi-experimental methods, time-series and state-space methods, and discrete choice and demand modelling.
• Ability to derive models from first principles and implement them in Python using probabilistic programming frameworks.
• Software discipline to produce maintainable, testable, reproducible code suitable for engineering teams.
• Fluency in inference diagnostics and model criticism, including convergence assessment, effective sample size, divergences, posterior predictive checks, cross-validation, prior sensitivity, and falsification tests on synthetic data.
• Understanding of identifiability, partial identification, and parameter recovery, including when to stop and reframe a client question.
• Experience designing and running experiments and validation frameworks, including A/B tests, geo and holdout designs, out-of-sample checks, back-testing, sensitivity analysis, and calibrated predictive uncertainty.
• Ability to translate model outputs into structured, versioned analytical artefacts, including mathematical specifications, data transforms, inference code, validation notebooks, uncertainty schemas, and deployment criteria.
• Familiarity with commercial data sources and limitations, including media platform data, panel data, point-of-sale data, and pricing and promotional data.
• Working knowledge of integrating model outputs into product interfaces and agentic workflows, including schemas, evaluation harnesses, and explicit uncertainty fields.
• Active use of AI-assisted coding tools to accelerate derivation, prototyping, and hypothesis testing while maintaining rigorous statistical practice.
• At least 3 years of applied statistical modelling experience in commercial, consulting-adjacent, or product environments; PhD, postdoctoral, internship, and consulting time count toward this threshold.
• Track record of building statistical models that real clients or products depend on, including 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 or equivalent commercial audiences.
• Experience working in agile, cross-functional teams, building defensible first-version models under real timelines, and iterating with validation.

Nice To Haves

• PhD in statistics or an equivalent quantitative field with dissertation-level applied inference work; completed or near completion, with completion within the last three years strongly preferred.
• Experience mentoring or managing data scientists, or setting analytical direction for a small practice.
• Dissertation, publications, or teaching and review experience in applied Bayesian inference, causal inference, econometrics, or marketing and pricing analytics.
• Direct production or consulting experience with marketing mix modelling, brand measurement, or pricing and promotional elasticity.

Responsibilities

• Set and own the statistical standard across all Growth AI Performance System modules, from problem framing through production handoff.
• Personally lead the hardest modelling problems, including problems requiring custom likelihoods, non-standard priors, partial identification analysis, or cross-model reconciliation.
• Derive models from first principles and implement correct, tested, reproducible estimators in Python using probabilistic programming frameworks.
• Diagnose identifiability, partial identification, convergence, and cases where a model cannot support the client question; reframe the problem when necessary.
• Design and enforce inference and validation standards, including MCMC and VI diagnostics, prior sensitivity, posterior predictive checks, simulation-based calibration, parameter recovery, out-of-sample validation, back-testing, and documented confidence bounds.
• Define how statistical outputs become structured, versioned data products for engineers and agentic workflows, including schemas, configuration, interpretable uncertainty measures, and explicit confidence bounds.
• Build and maintain a cross-model reconciliation framework for heterogeneous models that share derived commercial quantities.
• Drive methodological innovation in hierarchical Bayesian methods, causal and quasi-experimental inference, time-series and state-space methods, discrete choice modelling, and regularisation and shrinkage.
• Lead modelling across marketing mix and media ROI, brand equity and consumer choice, and pricing, promotional lift, and assortment.
• Stay current with commercial data sources and their modelling implications, including media platform data, brand tracking and panel data, point-of-sale data, and pricing and promotional data.
• Hire, mentor, and develop module-level Data Scientists and lead the data science practice.
• Present methodology, outputs, and recommendations to senior client stakeholders; explain assumptions, failure modes, and factors that could change recommendations.
• Lead working sessions that stress-test assumptions, interpret outputs, and challenge methodology; incorporate client feedback into methodological and platform improvements.
• Work with Data Engineering and platform Data teams to ensure model inputs are correctly structured, validated, and representative of commercial data.
• Define data requirements standards across modules, including schema specifications, quality thresholds, expected ranges, and edge cases.
• Evaluate data quality at the model-input layer, diagnose effects on model outputs, identify root causes, and partner with data teams on resolution.

Benefits

• Annual discretionary performance bonus.
• 401(k) plan with an annual employer contribution based on years of service; the listed contribution is 4.5%, increasing after three years of service, and fully vested upon start date.
• Bain pays 100% of individual employee premiums for medical, dental, and vision programs.
• Generous paid time off, including parental leave, sick leave, and paid holidays.
• Paid Life and Long-Term Disability insurance.
• The role may be eligible for other elements of discretionary compensation.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree