Principal Data Scientist- Decisioning at Citizens Financial Group

Job description

The Enterprise Data & Analytics organization is seeking a Principal Data Scientist to design, build, and improve quantitative models that strengthen customer relationships, improve decision quality, and drive measurable business outcomes. In this role, you will: • Lead the development of statistical, machine learning, and causal inference solutions from problem definition through deployment, monitoring, validation, and continuous refinement. • Evaluate targeting and decision performance using experimentation, counterfactual analysis, quasi-experimental techniques, and observational data to improve customer and business outcomes. • Develop methods that estimate incremental impact, treatment effects, and heterogeneous customer response to support more effective and economically sound decisions. • Translate ambiguous business objectives into defensible analytical problems, measurable outcomes, analytical requirements, and practical implementation plans. • Partner with data science, marketing, product, finance, risk, data, and technology teams to integrate models into governed business processes and production decision systems. • Synthesize analytic findings into a clear and approachable story and set of recommended actions, reducing complexity as appropriate for different audiences. • Communicate assumptions, tradeoffs, limitations, and recommendations clearly while maintaining appropriate documentation, controls, and ownership. • Guide the continuous improvement of analytical systems by incorporating experimental results, operational outcomes, and observed customer behavior into future models and targeting strategies. A successful incumbent will have: • Master’s degree in statistics, economics, engineering, computer science, or a related quantitative field; PhD preferred. • 10+ years of experience applying advanced analytics and delivering models through the full development lifecycle, including deployment, monitoring, governance, and ongoing improvement. • Deep knowledge of statistical modeling, causal inference, and experimental design, with a demonstrated ability to improve decision quality and targeting through estimation and counterfactual reasoning. • Experience estimating heterogeneous treatment effects and incremental value using randomized experiments, causal machine learning, quasi-experimental techniques, uplift modeling, and contextual bandits to support customer-level personalization and decisioning. • Experience designing, executing, and evaluating experiments, including randomization strategy, holdouts, guardrails, outcome measurement, and causal interpretation of results. • Strong Python and SQL skills (with 5+ years of applied experience each), including experience building reproducible analytical pipelines and working efficiently with large, complex datasets. • Experience with cloud computing platforms (preferably AWS) and ML-related services such as Amazon Sagemaker. • Ability to balance analytical rigor, interpretability, implementation complexity, and business usefulness under evolving priorities. • Strong stakeholder-management and communication skills, including the ability to explain complex analytical, experimental, and causal concepts in clear, unambiguous language. • Financial services experience and familiarity with related customer-level behavior, profitability, personalization, and relationship analytics preferred. Hours & Work Schedule • Hours per Week: 40 • Work Schedule: Monday - Friday • Hybrid: 4 days per week onsite and 1 day remote Pay Transparency The salary range for this position is $149,000 - $194,000 per year, plus an opportunity to earn an annual discretionary bonus. Actual pay is based on various factors including but not limited to the budget, work location, and relevant skills and experience. We offer competitive pay , comprehensive medical, dental and vision coverage, retirement benefits, maternity/paternity leave, flexible work arrangements, education reimbursement, wellness programs and more. Note, Citizens’ paid time off policy exceeds the mandatory, paid sick or paid time-away policy of every local and state jurisdiction in the United States. For an overview of our benefits, visit jobs.citizensbank.com .

Requirements

Must-haves

Lead the development of statistical, machine learning, and causal inference solutions from problem definition through deployment, monitoring, validation, and continuous refinement.
Evaluate targeting and decision performance using experimentation, counterfactual analysis, quasi-experimental techniques, and observational data to improve customer and business outcomes.
Develop methods that estimate incremental impact, treatment effects, and heterogeneous customer response to support more effective and economically sound decisions.
Translate ambiguous business objectives into defensible analytical problems, measurable outcomes, analytical requirements, and practical implementation plans.
Partner with data science, marketing, product, finance, risk, data, and technology teams to integrate models into governed business processes and production decision systems.
Synthesize analytic findings into a clear and approachable story and set of recommended actions, reducing complexity as appropriate for different audiences.
Communicate assumptions, tradeoffs, limitations, and recommendations clearly while maintaining appropriate documentation, controls, and ownership.
Guide the continuous improvement of analytical systems by incorporating experimental results, operational outcomes, and observed customer behavior into future models and targeting strategies.
10+ years of experience applying advanced analytics and delivering models through the full development lifecycle, including deployment, monitoring, governance, and ongoing improvement.
Deep knowledge of statistical modeling, causal inference, and experimental design, with a demonstrated ability to improve decision quality and targeting through estimation and counterfactual reasoning.
Experience designing, executing, and evaluating experiments, including randomization strategy, holdouts, guardrails, outcome measurement, and causal interpretation of results.
Strong Python and SQL skills (with 5+ years of applied experience each), including experience building reproducible analytical pipelines and working efficiently with large, complex datasets.
Experience with cloud computing platforms (preferably AWS) and ML-related services such as Amazon Sagemaker.
Ability to balance analytical rigor, interpretability, implementation complexity, and business usefulness under evolving priorities.
Strong stakeholder-management and communication skills, including the ability to explain complex analytical, experimental, and causal concepts in clear, unambiguous language.
Hours & Work Schedule
Hours per Week: 40
Work Schedule: Monday - Friday
Hybrid: 4 days per week onsite and 1 day remote

Nice-to-haves

Master’s degree in statistics, economics, engineering, computer science, or a related quantitative field; PhD preferred.
Financial services experience and familiarity with related customer-level behavior, profitability, personalization, and relationship analytics preferred.

Application prep

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Keywords to mirror

machine learningpythonsqlawsml

Platform: oracle-cloud
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

Paid Time Off

Wellness