Manager, Data Science - North America Marketing

Coca-Cola•Atlanta, GA

2d•Onsite

About The Position

Reports To: Sr. Director I, Modeling & Measurement (NAOU Marketing) Role Purpose The Manager, Modeling Analyst plays a hands-on role in executing advanced analytics and modeling to support data-driven decision-making across NAOU Marketing. This position focuses on applying a range of analytical and modeling techniques to understand business performance, diagnose drivers, and inform strategy. You will work across diverse use cases, including marketing effectiveness, customer behavior, forecasting, and business diagnostics. The role requires both technical depth and business curiosity— translating complex analyses into clear, actionable insights. This is an ideal opportunity for someone who enjoys solving problems with data and contributing directly to business outcomes

Requirements

Bachelor’s degree in a quantitative field (e.g., Statistics, Economics, Mathematics, Computer Science, Engineering).
3+ years of experience in data analysis or data science.
Strong experience working with large datasets, applying statistical methods, and generating business insights.
Proficiency in SQL and at least one programming language (Python or R).
Experience with data manipulation, statistical analysis, and visualization.
Strong foundation in statistics and analytical thinking is essential.
Understanding of key business and performance metrics (e.g., growth drivers, ROI, conversion, customer behavior).
Ability to connect data insights to business decisions and priorities.
Strong focus on accuracy, data validation, and quality control.
Ability to work with imperfect data and apply sound judgment in analysis.
Ability to communicate complex analytical concepts in simple, clear terms to non-technical audiences.
Strong written and verbal communication skills.
Comfortable working independently and as part of a team.
Able to manage multiple priorities, meet deadlines, and proactively engage with stakeholders.
Curiosity and willingness to learn new tools, techniques, and business domains.
Ability to adapt in a fast-paced, evolving environment.
All applicants must be currently authorized to work in the United States on a full-time basis and must not require The Coca-Cola Company's sponsorship to continue to work legally in the United States.

Nice To Haves

Advanced degree is a plus but not required.
Exposure to marketing, sales, or customer data is valuable but not required.
Experience with a range of modeling approaches (predictive modeling, forecasting, segmentation, experimentation, etc.) is preferred.
Familiarity with tools such as Tableau, Power BI, or similar is a plus.

Responsibilities

Execute Data Analysis & Modeling Projects: Perform structured analyses and build models to address key business questions.
Work with large datasets to explore patterns, engineer features, and apply appropriate analytical techniques (e.g., regression, classification, time series, or other statistical/ML methods).
Ensure a high standard of rigor, including data validation, model testing, and iteration.
Support Model Development & Validation: Partner with senior team members to develop and refine advanced models.
Contribute to data preparation, methodology selection, and model validation.
Perform diagnostic analyses to evaluate model performance and identify opportunities for improvement.
Generate Insights and Reports: Translate analytical outputs into clear, actionable insights.
Develop concise deliverables (slides, summaries, visuals) that communicate what is happening, why it matters, and what actions should be taken.
Support stakeholder discussions and respond to follow-up questions with data-backed reasoning.
Collaborate with Marketing Teams for Data Gathering & Context: Work closely with Marketing, Finance, and other teams to gather data inputs and understand business context.
Ensure analyses reflect real-world conditions, including promotions, operational changes, and external factors.
Maintain Dashboards and Ongoing Analytics Tools: Support ongoing reporting, dashboards, and analytical tools.
Refresh data, validate outputs, and identify opportunities to improve automation, usability, and insight generation.
Contribute to Data Quality and Governance: Promote strong data practices by identifying inconsistencies, validating inputs, and collaborating with partners to improve data quality.
Help ensure reliable and consistent datasets for analysis.