Principal Data Scientist- Decisioning

Citizens • Columbus, OH

4d • $149,000 - $194,000 • Hybrid

About The Position

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. The role leads statistical, machine learning, and causal inference solutions from problem definition through deployment, monitoring, validation, and continuous refinement. It evaluates targeting and decision performance using experimentation, counterfactual analysis, quasi-experimental techniques, and observational data, and develops methods to estimate incremental impact, treatment effects, and heterogeneous customer response.

The Principal Data Scientist translates ambiguous business objectives into defensible analytical problems, measurable outcomes, requirements, and implementation plans. The role partners across data science, marketing, product, finance, risk, data, and technology to integrate models into governed business processes and production decision systems. It also communicates findings and recommendations to different audiences, documents assumptions and limitations, and improves analytical systems using experimental results, operational outcomes, and observed customer behavior.

Requirements

• Master’s degree in statistics, economics, engineering, computer science, or a related quantitative field.
• 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 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 for 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 in each.
• Experience building reproducible analytical pipelines and working efficiently with large, complex datasets.
• Experience with cloud computing platforms and ML-related services.
• 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 clearly.

Nice To Haves

• PhD in statistics, economics, engineering, computer science, or a related quantitative field.
• Experience with AWS and ML-related services such as Amazon Sagemaker.
• Financial services experience and familiarity with customer-level behavior, profitability, personalization, and relationship analytics.

Responsibilities

• Lead 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.
• Develop methods to estimate incremental impact, treatment effects, and heterogeneous customer response to support effective and economically sound decisions.
• Translate ambiguous business objectives into analytical problems, measurable outcomes, 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 clear, approachable stories and recommended actions for different audiences.
• Communicate assumptions, tradeoffs, limitations, and recommendations clearly, while maintaining appropriate documentation, controls, and ownership.
• Improve analytical systems by incorporating experimental results, operational outcomes, and observed customer behavior into future models and targeting strategies.

Benefits

• Competitive pay and opportunity to earn an annual discretionary bonus.
• Comprehensive medical, dental, and vision coverage.
• Retirement benefits.
• Maternity and paternity leave.
• Flexible work arrangements.
• Education reimbursement.
• Wellness programs.
• Paid time off policy exceeding mandatory paid sick or paid time-away policies in every local and state jurisdiction in the United States.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal