Job Description Summary:
Digital products play a central role in how we create value for customers, support the teams who serve them, and shape the consumer experience.
Our product organization brings together small, empowered teams that move with clarity, speed,
and purpose, enabling digital to be a meaningful source of advantage across Coca-Cola’s North America Operating Unit.
Our work spans customer journeys, service delivery, sales workflows, and the platforms that connect them. We are raising our standards for product craft and rebuilding the systems behind these experiences.
As a Tech Lead specializing in Machine Learning and Data Engineering, you will lead the technical direction for end-to-end ML capabilities that ship as part of our product, while also ensuring the data foundations (events, pipelines, feature tables, and governance) are reliable and scalable. You’ll partner with Product, Design, Data Science/Analytics, and platform teams to frame problems, define success metrics, and guide solutions from data modeling and feature engineering through model training, deployment, monitoring, and iteration. This is a hands-on leadership role for engineers who can set standards, unblock teams, and drive execution across the ML and data stack without formal people-management responsibilities.
What You Will Work On:
Build ML-powered data products that model transaction drivers and surface optimized actions as insights to be embedded within integrated internal and external digital experiences that shape how our beverage brands activate across retail, foodservice, and digital channels. The success of our products is tied directly to measurable transaction lift at the point of sale, a primary objective of the North America Operating Unit and The Coca-Cola Company as a whole.
How We Work
You’ll be part of a dedicated, cross-functional team (Product, Design, Engineering) that is:
Empowered to solve problems, not just build features
Accountable for outcomes, not output
Collaborative by default, from discovery through delivery
Continuously learning, using data and customer insight to improve
Key Responsibilities
Technical direction for a product ML domain: problem framing, approach selection, evaluation strategy, and iteration
Data and feature foundations: event/telemetry definitions, transformation logic, feature/label tables, and training/serving consistency
Production ML systems: deployment patterns (batch/online), model performance/latency tradeoffs, and operational readiness
Quality and reliability: data quality checks, model monitoring (drift/performance), alerting, and runbooks
Engineering standards: design reviews, code review quality, documentation, and reusable patterns for ML + data workflows
Mentorship and enablement: coaching engineers through complex work and unblocking delivery across teams
Develop, Train & Evaluate Models
Build baselines and iterate on model approaches appropriate to the product problem (e.g., gradient boosting, deep learning, ranking)
Lead feature engineering with strong data discipline: define entities and joins, validate labels, and ensure training/serving consistency
Run experiments and evaluate models using sound methodology (train/validation splits, cross-validation as appropriate, error analysis)
Document findings and recommendations clearly for technical and non-technical audiences
Deploy & Operate Models in Production
Deploy models to production (batch and/or real-time) with attention to latency, reliability, and cost
Implement monitoring for upstream data and feature freshness/quality, drift, and model performance; define alerting and response playbooks
Automate repeatable training and evaluation workflows (versioning, reproducibility, and artifact tracking)
Participate in incident response and post-incident reviews when model behavior impacts customers or operations
Establish reusable patterns for feature pipelines (batch/stream), backfills, and schema evolution; raise the bar through design reviews
Define and reinforce standards for data governance and responsible ML (PII handling, access controls, data contracts, bias/fairness considerations)
Partner with platform teams on the data stack (warehouse/lakehouse, streaming, orchestration) and MLOps tooling (feature stores, training infrastructure, deployment, monitoring)
What We’re Looking For
Applied ML fundamentals: Understands supervised learning, evaluation metrics, and common failure modes
Strong programming skills: Comfortable in Python and writing production-quality code (testing, readability, performance)
Data intuition: Able to analyze datasets with SQL and/or Python, spot issues, and reason about bias/leakage
Product mindset: Cares about measurable impact, guardrails, and user experience—not just model metrics
Cross-functional collaboration: Partners with Product, Data Science, and Engineering to ship and iterate on ML features
MLOps + data platform fluency: Comfortable with deployment, monitoring, reproducibility, and the pipelines/warehouses/streams that feed models
Key Qualifications
6+ years of experience in machine learning engineering, data engineering, or software engineering, including leading technical direction for ML/data systems
Demonstrated ownership of model development and evaluation, including metric selection, error analysis, and experimentation discipline
Strong engineering fundamentals in Python (and SQL) with production practices (testing, reviews, CI/CD); familiarity with ML frameworks (e.g., PyTorch/TensorFlow) and data tooling (e.g., Spark, dbt, Airflow/Dagster) is preferred
Experience shipping and operating ML systems in production, including model monitoring, rollback/retraining strategies, and coordination with upstream data/feature pipelines
Familiarity with data platforms (data warehouse/lakehouse concepts), and exposure to orchestration/ETL tools (e.g., Microsoft fabric, Airflow, dbt, Spark)
Preferred Qualifications
Experience building product ML systems such as personalization, recommendations, ranking, forecasting, or NLP
Experience with experimentation and measurement (A/B testing, uplift/impact analysis, online guardrails)
Experience with feature pipelines or feature stores, and patterns for training/serving consistency
Experience designing and operating data pipelines that power ML (batch and streaming), with clear SLAs for freshness and quality
Experience with lakehouse/warehouse modeling for analytics and ML (dimensional/event models, backfills, schema evolution, data contracts)
Demonstrated tech lead behaviors: driving design reviews, setting standards, mentoring engineers, and aligning stakeholders on tradeoffs
Experience with model and data observability (drift detection, performance monitoring, dashboards/alerting)
Familiarity with responsible AI and data privacy considerations (PII handling, access controls, model risk)
Experience with production infrastructure (e.g., Docker/Kubernetes) or workflow tooling (e.g., Airflow, Dagster) used to run ML jobs
Familiarity with modern engineering practices (CI/CD, testing, observability)
Education
Bachelor’s degree in Computer Science, Engineering, or a related field
Equivalent practical experience is equally valued
Who Thrives Here
Enjoy leading through influence—turning ambiguous problems into clear ML + data plans and helping others execute
Communicate clearly across Product, Data Science, Analytics, and Engineering—especially around definitions, tradeoffs, and risk
Take pride in raising the bar: reliable models and data pipelines, strong documentation, and operational follow-through
Who This Role Is Not For
This role may not be the right fit if you:
Want to focus only on research prototypes or only on data pipelines (instead of owning end-to-end product ML systems)
Avoid leading through influence (design reviews, alignment, mentorship) and prefer not to set or uphold technical standards
Prefer to avoid operational responsibility for model and data health (monitoring, incidents, data quality/freshness, and continuous improvement)
Skills:
Agile Methodology, Atlassian JIRA, Business Processes, Business Process Modeling, Cloud Platform, Communication, Data Flow Diagram, DevOps, Digital Transformation, Enterprise Architecture Framework, Enterprise Content Management (ECM), Java (Programming Language), Kotlin Programming Language, Microsoft Office, Microsoft SharePoint, Mobile Applications, Object-Oriented Programming (OOP), User Experience (UX)Pay Range:
United States of America: 171,000 USD - 198,000 USDBase pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered.
Annual Incentive Reference Value Percentage:
30Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target.
Location(s):
United States of AmericaCity/Cities:
AtlantaTravel Required:
00% - 25%Relocation Provided:
YesJob Posting End Date:
June 24, 2026Our Purpose and Growth Culture:
We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class. When we collect your personal information as part of a job application or offer of employment, we do so in accordance with industry standards and best practices and in compliance with applicable privacy laws.