Leads the development, deployment, and optimization of predictive models across all Retail, Business Banking and Wealth LOBs that drive customer growth and profitability.
Owns end-to-end model lifecycle from data sourcing through validation and approval.
Direct leadership of assigned data science team.
Develop and own predictive models, including propensity models, response models, customer value models, Marketing Mix Models (MMM) and segmentation models.
Select appropriate methodologies (logistic regression, GBM, random forest, etc.) based on business use case.
Communicate models with senior leaders and business partners in a clear and concise manner.
Manage ongoing model execution, validation, enhancement and governance of model suite.
Partner with Marketing Analytics and business partners to leverage models for targeting strategies to efficiently grow the business.
Define and curate model-ready feature sets from internal and 3rd party data sources.
Engineer model features that capture customer behavior, engagement, credit signals, channel interaction and channel activity.
Leverage tools such as Databricks for MLOps.
Ensure models are compliant with Model Risk Management (MRM).
Lead and mentor junior data scientists
Define modeling standards and best practices across the team.
Drive consistent tool usage, methodologies and MLOPs processes
Manage and develop champion/challenger models and adjust models accordingly.

Bachelor’s degree and a minimum of 7 years related experience, or in lieu of a degree, a combined minimum of 11 years higher education and/ or work experience, including a minimum of 7 years related experience
Minimum of 2 years managerial, supervisory and/or work leadership experience
Intermediate experience working with multiple statistics and following data science principles such as AB testing, sample selection, hypothesis testing, and modeling bias
Intermediate proficiency with pertinent statistical software and languages and tools
Experience with various hybrid databases both on premise and in the cloud
Intermediate level knowledge of Structured Query Language (SQL) and Not Only Structured Query Language (nSQL)
Expert understanding of modeling techniques such as Bayesian modeling, Classification models, Cluster analysis, Neural Network, Non-parametric methods, and Multivariate statistics
Experience analyzing large data sets
Proficient in data scient platform programming languages such as Python, R, SQL, SAS
Proficient with data platforms such as Snowflake and Databricks

Lead Data Scientist – Marketing Predictive Analytics

Key skills