The Coca-Cola Company · 3 hours ago
Predictive Modeling Director
Atlanta, GA
Full-time
Onsite
Director/Executive
$169K/yr - $200K/yr
8+ years expThe Coca-Cola Company is seeking a Predictive Modeling Director responsible for designing and implementing advanced predictive models to optimize marketing performance. This role involves leading the development of statistical and machine learning models, collaborating with stakeholders, and translating complex data insights into actionable marketing strategies.
Consumer GoodsFast-Moving Consumer GoodsFood and BeverageManufacturing
Responsibilities
Lead Development of Predictive Models: Design and develop statistical and machine learning models to tackle key marketing challenges. This includes taking ownership of our marketing mix modeling (MMM) efforts – updating and enhancing econometric models that measure the impact of different marketing inputs on sales and other outcomes. You will also spearhead other predictive modeling initiatives, such as customer lifetime value models, churn/retention models, segmentation and clustering analyses, and demand forecasting for marketing planning. Starting from business hypotheses or questions, manage the full modeling process: data gathering and preprocessing, variable selection, model building, validation, and iteration. Ensure models are robust, explainable, and actionable, providing not just predictions but insights into drivers (e.g., which media channels are most effective at driving incremental sales)
Implement Modeling Solutions for Optimization: Translate model outputs into practical applications that marketers can use. For example, develop tools or frameworks that leverage model results to simulate scenarios (like a “what-if” tool for adjusting media spend across channels) and recommend optimal allocations. Work with our technology partners to automate or integrate models into dashboards or planning systems, enabling real-time or regular access to model insights for stakeholders. Ensure that modeling solutions are user-friendly and can be run/updated with appropriate frequency (e.g., MMM updated quarterly) to stay relevant. Additionally, oversee any external modeling vendors or consultants (such as those providing third-party MMM services or software) to ensure their work aligns with our objectives and quality standards
Collaborate with Stakeholders to Scope & Answer Business Questions: Engage directly with Marketing, Human Sciences and IMX teams to understand their needs and frame the problems that modeling can help solve. For instance, partner with Media and IMX directors to define the scope of an MMM study (which brands, what time period, which metrics) or a pricing elasticity analysis. Regularly meet with brand managers, connection planners, and others to gather input on what decisions they are trying to inform (e.g., “How much should we shift from TV to digital?” or “Which consumer segments should we prioritize for a new campaign?”). Ensure each model or analysis you lead is grounded in a clear use-case and that you and your team clearly communicate the assumptions and limitations. After delivering model results, work with those stakeholders to interpret the findings and brainstorm how to apply them in marketing strategies. Your role is as much about asking the right questions as it is about crunching numbers, ensuring modeling efforts remain business-centric
Ensure Data Accuracy & Modeling Best Practices: Manage the data inputs and statistical rigor for all modeling projects. Work closely with Data Engineering or IT teams to access and prepare the necessary data (e.g., historical spend and sales data, customer-level data from CRM, media impressions, promotional calendars). Perform thorough data cleaning and exploratory analysis to validate that the data makes sense before modeling. When building models, follow best practices for avoiding bias and overfitting – for example, using out-of-sample validation, significance testing, and business sense checks. Calibrate models using back-testing or holdout samples to verify they accurately predict outcomes. Document model methodologies and assumptions, and maintain a library of models and code for reproducibility. Continuously monitor model performance over time and refresh models as new data comes in or as market conditions change (for instance, if a new media channel emerges, incorporate it into the mix model)
Communicate Insights & Recommendations: Although very technical in nature, this role requires translating model results into clear, non-technical insights and recommendations. After completing an analysis, synthesize the key findings: e.g., “TV ads are providing diminishing returns beyond X GRPs, while digital video still shows growth in ROI,” or “Segment A has 20% higher lifetime value than average, suggesting we increase investment in loyalty for this group.” Create compelling presentations and visualizations to tell the story from the data, highlighting how the insights can improve marketing outcomes. Present these insights to marketing leaders and working teams, adapting your communication to the audience (detailed for analytic peers, high-level and outcome-focused for executives). Often, you will provide decision support by clearly outlining options informed by the model (e.g., “Based on our model, Plan B would likely yield $Y more revenue than Plan A, albeit with higher risk in segment Z”). Aim to establish trust in the models so that marketing partners come to rely on them for planning and optimization cycles
Mentor and Manage Analytical Talent: Lead a small team of modelers and data analysts, providing hands-on guidance and oversight. Delegate projects effectively based on team members’ strengths, and review their work critically to ensure quality and accuracy. Coach the team on advanced analytical techniques and help troubleshoot statistical or data challenges that arise. Set a high bar for analytical excellence and continuous improvement – encourage team members to explore new methods (like evolving an MMM to incorporate digital attribution or testing a machine learning approach for segmenting consumers) and share knowledge within the team. Additionally, contribute to the broader analytics community in NAOU (for example, collaborating with the Director of Media Measurement or other analytics leads) to share learnings and avoid silos. Help develop junior talent into future experts by exposing them to different types of modeling projects and ensuring they understand the marketing context and not just the math
Qualification
Required
Bachelor's degree in Statistics, Applied Mathematics, Computer Science, Engineering, or another field with a strong quantitative focus
Demonstrated academic or professional training in advanced statistical modeling and machine learning techniques is expected
8-10+ years of experience in data science, marketing analytics, or a similar function, with direct experience building predictive models that inform business strategy
Experience within the CPG or marketing analytics consulting industry is highly valued, especially if you have worked on marketing mix models or consumer predictive analytics
A track record of translating complex modeling results into actionable business recommendations is required
Advanced proficiency in statistical analysis and modeling tools
Strong programming skills in languages like Python or R for data analysis and model development
Experience with statistical libraries (pandas, scikit-learn, statsmodels in Python; or equivalent in R)
Deep knowledge of regression analysis, time-series forecasting, machine learning algorithms (regression, classification, clustering, tree-based models, etc.) as applied to marketing problems
Experience building marketing mix models or multi-touch attribution models is a must
Comfortable working with large datasets; able to write SQL to extract and manipulate data
Familiarity with data visualization and BI tools (Tableau, Power BI) to present model findings
Ability to quickly learn and use specialized analytics software (for example, Nielsen or third-party MMM tools, if used) and to evaluate their output critically
Strong understanding of marketing concepts and levers
Able to contextualize models within the marketing mix and consumer journey
Ability to interpret model results in business terms (e.g., diminishing returns, saturation point, elasticity) and connect them to recommendations like budget reallocation or targeting changes
Awareness of industry trends in marketing analytics
Excellent problem-solving skills with the ability to frame ambiguous marketing questions into concrete analytical tasks
Attention to detail to catch data issues or anomalies in model results
Demonstrated capacity to not just produce data outputs, but to also generate insights
Ability to explain complex analytical concepts to non-technical audiences in a compelling way
Strong communication skills, both written (presentations, documentation) and verbal
Experience presenting findings or recommendations to marketing or business leaders is required
Collaborative working style – open to input from others and adept at working in cross-functional teams
Comfortable managing multiple stakeholders and projects, with strong project management and prioritization abilities to meet deadlines in a fast-paced environment
Self-motivated and proactive in identifying opportunities where modeling can add value
Takes ownership of projects and follows through on commitments
Demonstrated leadership potential, whether through formally managing team members or informally guiding peers
Skilled at mentoring junior analysts, giving constructive feedback and fostering growth
A passion for continuous learning in the analytics field, staying up-to-date with new methods or tools
High ethical standards regarding data privacy and responsible use of data in modeling
Preferred
A Master's degree in Data Science, Analytics, Economics, or MBA with analytics specialization is preferred
Leadership experience (formal or informal) such as project lead or people manager for analysts/modelers is preferred, indicating ability to manage projects and mentor others
Benefits
A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Company
The Coca-Cola Company
The Coca-Cola Company is a soft drink manufacturer & distributor that makes a variety of soft drinks, including diet coke and regular coke.
10001+ employees
H1B Sponsorship
The Coca-Cola Company has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (17)
2024 (23)
2023 (13)
2022 (17)
2021 (16)
2020 (9)
Funding
Current Stage
Public CompanyTotal Funding
unknown1919-09-05IPO
Leadership Team
Henrique Braun
Chief Operating Officer
Jennifer Henry
Senior Vice President, Strategy and Corporate Development
Recent News
2026-01-16
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