Lead Data Scientist, Financial Enablement Analytics

Chick-fil-A, Inc.•Atlanta, GA

9d•Hybrid

About The Position

The Financial Solutions Lab team within Financial Enablement supports business partners with data engineering, analytics, and AI expertise. This role will support the team’s existing business clients with ad-hoc advanced analysis and/or production predictive machine learning (ML) pipelines. In addition, this role will support the Financial Services department’s AI automation needs, combining generative artificial intelligence (GenAI) and ML through close collaboration with engineering partners to meet business requirements.

Requirements

Has strong analytical and research skills
Has a strong understanding of statistics and statistical modeling.
Has a basic understanding of modern ML and its mathematical underpinnings.
Has moderate programming skills in SQL and [Python and/or R].
Understanding of deep learning and artificial neural network architecture underlying modern GenAI models
Familiarity with visualization methods/tools (e.g. Python Matplotlib, R ggplot2, Streamlit, Shiny)
Has strong written and verbal communication skills.
Is a quick and versatile learner and a proven problem solver.
Has a keen eye for system thinking and process design, especially with respect to scalability and automation.
Has a keen eye for detail and thoughtful investigation of data.
Has a focus on creating impactful change.
Has the ability to maintain a focus on the business context and key decisions to be made.
Has the ability to manage multiple projects and deadlines simultaneously.
Has experience partnering well with cross-functional teams, being highly collaborative with the ability to build strong relationships.
Bachelor's Degree
Quantitative or technical degree program, including but not limited to: data science, statistics, mathematics, engineering, economics. 3.2 GPA required
3

Nice To Haves

Understanding of large language model (LLM) and neural network (NN) architecture.
Has some experience using the AWS big data technology stack.
Has any experience developing ML or advanced statistical models.
Has some professional experience in the restaurant, retail, or hospitality industry.
Has any experience with Model Operations tools and processes.
Experience with GenAI coding assistants
MBA or Masters’ Degree in a quantitative field, like Business Analytics, Mathematics, Statistics, or other similar fields, and 3.2 GPA
5

Responsibilities

Core Modeling & Analysis - Leverage strong exploratory data analysis skills to analyze and integrate a wide range of data. Apply standard methods, models, algorithms, and/or simulations to solve simple to moderately complex business problems, performing ad-hoc analysis as necessary.
Demonstrate an understanding of statistics and ML methodologies, working under the direction of more senior data scientists when asked to take on more complex work.
Deliver systems that automate, expedite, simulate, predict, validate, audit, diagnose, reconcile, and/or explain.
Data Preparation & Feature Development - Work closely with Data Engineers to define data requirements, evaluate source data quality, determine needed data transformations, and execute candidate feature engineering.
Develop and manage ETL pipes in support of specific analytic project work, including understanding and advocating for mixed-use ML/GenAI systems’ data requirements.
Deployment, Monitoring & Maintenance - Work closely with Deployment Engineers to refactor model and feature code for deployment via the Model Ops toolchain, test the model workflow pre-deployment, and deploy and evaluate the model workflow into production.
Manage maintenance, retraining, or quality control on models actively in production. This includes evaluating the accuracy of predictive ML models and LLM responses—recognizing the non-deterministic nature of GenAI output—and suggesting tuning or feature engineering to address inaccuracies.
Measure and monitor automated pipeline performance using standard metrics (e.g., accuracy, AUC, bias, calibration, pinball loss, stability, etc.).
Quality, Standards & Tools - Act as an individual contributor responsible for implementing data science best practices. Own and be accountable for model and code quality and documentation for executed project work.
Leverage pre-defined cloud compute resources and technology (Databricks, AWS, GitHub)
Collaboration & Stakeholder Management - Interact with business partners in a hybrid work environment to understand their needs and influence others towards an effective solution.
Partner closely with Engineering on GenAI component implementation and to utilize data science tools.
Communicate with, coordinate with, and train users and stakeholders about the advanced analytics products being built and maintained.