As a Data Scientist Associate in the Consumer Bank Sales Science team, you will help develop and scale data-driven approaches that connect customer demand, branch capacity, and sales outcomes to inform staffing decisions across our branch network. You’ll work on analytically rich problems—forecasting demand, modeling capacity, and evaluating tradeoffs—to help teams make decisions that improve customer experience and business performance. You’ll partner closely with colleagues across branch operations, workforce management, finance, product, and analytics to translate real-world constraints into solutions that can be put into practice. You’ll communicate insights clearly to both technical and non-technical stakeholders and help build repeatable tools and measurement frameworks. You’ll have opportunities to expand your technical depth and broaden your business impact through mobility across adjacent analytics and data science problem spaces within the firm.
Key responsibilities
- Develop and maintain branch staffing models to optimize banker allocation, leveraging capacity modeling, demand forecasting, and constraint-based optimization techniques.
- Design and evaluate staffing strategies (coverage, schedules, skill mix) and quantify expected impact on sales performance, customer outcomes, and productivity.
- Build scenario-planning tools to support “what-if” decisions on branch footprint, traffic changes, operating hours, and banker role design.
- Establish KPI frameworks and reporting to monitor staffing interventions (e.g., conversion, appointment utilization, wait-time proxies, productivity, customer experience).
- Lead analytical assessments and experiments/pilots to measure the effectiveness of staffing changes and translate results into clear recommendations and actions.
- Partner with data and platform teams to source, curate, and document datasets; create reusable feature sets/pipelines to enable repeatable analytics and model deployment.
- Collaborate with cross-functional stakeholders (branch operations, workforce management, sales leaders, product partners) to align requirements and communicate insights to technical and non-technical
Required qualifications
- 1–3 years of experience (or strong internship/co-op experience) applying data science to business problems.
- Strong programming skills in Python and SQL; ability to write clean, testable code.
- Solid foundation in statistics and predictive modeling (regression/classification, time series basics, model evaluation).
- Experience translating ambiguous problems into analytical approaches and communicating results clearly.
Preferred qualifications
- Experience with optimization (linear/integer programming, heuristics) and/or forecasting (hierarchical or time-series models).
- Familiarity with operational analytics concepts (capacity, queues, service levels, workforce planning).
- Experience with model deployment patterns (batch scoring, APIs), MLOps fundamentals, and version control (Git).
- Experience with large-scale data environments (e.g., Spark) and dashboarding/visual analytics tools.