Marketing Mix Optimization – Principal Data Scientist

Citizens Bank

3d • Hybrid

About The Position

There’s no better time to be a Citizen! We are growing and looking to add to our Marketing Analytics team.
The Marketing Analytics team supports Enterprise Marketing with data, analytics, and insights that inform our customer marketing and product strategy across all Lines of Businesses. Marketing Analytics is responsible for analyzing the performance of marketing campaigns across online and offline channels and provides analytical support throughout the marketing campaign lifecycle including pre-campaign sizing, list production, experimental test design, measurement, optimization, and forecasting.
We are seeking a Principal Data Scientist with strong analytical and algorithmic skills to optimize our marketing investment strategies. In this role, you will design and build advanced decision support systems that facilitate scenario planning for marketing investment decisions, including constrained optimization solutions that leverage a suite of marketing mix models. You will develop relationships with other data scientists as well as partners in Marketing, Product, Finance and Technology Services to collaboratively deliver transformational optimization capabilities.

Requirements

• 8+ years of experience in quantitative analytics including marketing analytics, financial modeling, applied statistics, or equivalent.
• 4+ years of experience delivering quantitative decision tools for business applications.
• Proven experience turning business problems into rigorous analytic solutions by applying critical thinking and advanced technical & statistical programming techniques.
• Expertise in Python with 5+ years of applied experience.
• Proficient with one or more optimization modeling packages and solvers (e.g. GAMS/CONOPT, CPLEX, SCIP, Pyomo, SciPy).
• Expertise translating statistical models into scenario planning and optimization and solve non-linear, constrained optimization problems.
• A deep understanding of the theory and application of a variety of statistical and machine learning methods and algorithms, including optimization under uncertainty, forecasting, time series analysis, and Bayesian methods

Nice To Haves

• Strong sense of ownership, relentless curiosity, and self-driven approach to problem solving.
• Experience in data and analytics in Banking and Financial Services
• Strong written and verbal communication skills required with an ability to successfully communicate analytic results, insights, and resulting business implications to technical and non-technical audiences.
• Ability to work in a team environment and collaborate with colleagues who have a background in statistics, database development/maintenance, and information technology.

Responsibilities

• Architect and develop marketing investment optimization frameworks that translate marketing mix models and other analytic outputs into actionable budget allocation and scenario planning across channels, products, markets and time.
• Design and implement non‑linear, constrained optimization solutions that incorporate real‑world business constraints (e.g., budgets, channel minimums/maximums, pacing, and strategic priorities) to support high‑stakes marketing investment decisions.
• Serve as a technical authority on optimization and decision science, guiding best practices in objective function formulation, constraint design, solver selection, and performance validation.
• Partner closely with Marketing, Finance, Product, and Technology stakeholders to frame business questions into well‑defined optimization problems and translate analytical results into clear, decision‑ready recommendations.
• Build robust, scalable decision support tools that enable repeatable scenario analysis and are suitable for operational use by analytics, marketing and business teams.
• Lead the strategic roadmap for marketing optimization capabilities by identifying gaps, prioritizing enhancements, and aligning analytical solutions with evolving business needs.

What This Job Offers

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