Marketing Mix & Attribution Modeling Lead- Data Science Manager

Citizens Bank•Columbus, OH

16d•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. The Marketing Mix & Attribution Modeling Lead will manage a team of data scientists leveraging advanced statistical modeling techniques to lead the development of a suite of Marketing Mix Models and Multi-Touch Attribution models to holistically measure the incremental impact that marketing has on financial outcomes and build the framework to scenario plan and optimize marketing investments across products, marketing channels, markets, and time. This person will have significant interactions with other colleagues in the Marketing Analytics team and is expected to provide analytical thought leadership and bring cutting edge statistical knowledge to solve complex marketing problems. Deep technical skills, ability to continuously learn from industry and academic work, and strong communication skills will be determining factors in selection. Our preference would be to have a chosen candidate with onsite capabilities in the greater Boston MA or Columbus OH areas, however we are open to remote employment within the United States for the ideal candidate.

Requirements

8+ years of experience in quantitative analytics including marketing analytics, financial modeling, applied statistics, or equivalent.
5+ years of experience in continuous and discrete dependent variable regression-based modeling.
5+ years of experience coding in SQL, Python and maintaining standalone Python-based applications.
3+ years of hands-on experience programming marketing mix modeling or comparable solutions in Python.
Proven ability to lead high-performing teams through influence, accountability coaching and talent development.
Expertise translating statistical models into scenario planning and optimization tools leveraging GAMS, PyOpt or a comparable platform that solves non-linear, constrained optimization problems.
Expertise on Time-Series Modeling, with demonstrated ability to quantify uncertainty and model complex hierarchical data using Bayesian methods.

Nice To Haves

Exceptionally strong technical background with deep knowledge of statistical areas including ANOVA, Multiple Regression, Logistic Regression, Time-Series Modeling, Principal Component Analyses, Decision Trees, Clustering, etc.
Strong sense of ownership, relentless curiosity, and self-driven approach to problem solving.
Experience in data and analytics in Banking and Financial Services
Experience working with model risk governance in terms of documenting and validating statistical models used for making business decisions.
Strong written and verbal communication skills required with an ability to successfully communicate analytic results, insights, and resulting business implications to non-technical business partners.
Ability to work in a team environment and collaborate with colleagues who have a background in statistics, database development/maintenance, and information technology.

Responsibilities

Build, maintain, and evolve market level marketing mix models to estimate the causal impacts of marketing activities on Citizens’ financial outcomes in the short and long term
Manage and lead the model development lifecycle for a suite of Marketing Mix Models, including data wrangling, exploratory data analysis, feature engineering and variable transformations, econometric model development, model validation, documentation, model governance management and monitoring.
Decompose models and derive marketing “due-tos,” attributing financial outcomes to marketing impressions and spend. Derive marketing performance KPIs including Cost per Incremental Account (CPIAs) and Return on Marketing Investment (ROMIs).
Leverage non-linear constrained optimization to operationalize the suite of Marketing Mix Models and develop a scenario planning and optimization tool that can be used by the Marketing Analytics and Marketing teams to optimize marketing investments across products, channels, markets, and time.
Automate the model development process to scale model building across all Citizens’ Consumer Lines of Businesses.
Develop multi-touch attribution models using data driven statistical approaches beyond heuristic or rules-based models.
Partner with Marketing Analytics SPOCs, Media Analytics & Insights colleagues, and Enterprise Marketing stakeholders to derive actionable insights and recommendations. Find opportunities to optimize the marketing mix and maximize NPV given a budget and/or minimize budget given a NPV goal.
Become an expert across marketing channels, customer segments and key markets tracked by Citizens’ Enterprise Marketing team to contextualize business activities and priorities.
Document and communicate technical/analytic results, insights, and recommendations to both technical and non-technical audiences.
Collaborate with data scientists and analysts on our team to brainstorm solutions, understand data availability and tools, provide project updates, and share ideas.