Job Description Summary: 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. Depending on the business problem, solutions may use traditional machine learning and predictive models, deep learning, transformers, computer vision, retrieval-augmented generation (RAG), or combinations of these approaches.
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 - Analyze and integrate structured and unstructured data from enterprise platforms, customers, and external data providers.
- Build scalable data preparation and feature engineering pipelines for ML applications.
- Develop predictive and recommendation models using appropriate statistical and machine learning techniques.
- Build baselines and iterate on model approaches appropriate to the product problem (e.g., gradient boosting, deep learning, ranking)
- Run experiments and evaluate models using sound methodology (train/validation splits, cross-validation as appropriate, error analysis)
- Build reliable training and inference pipelines for batch and near-real-time use cases.
- Develop APIs and services that expose model predictions to web, mobile, CRM, and other enterprise applications.
- Establish rigorous model evaluation, testing and validation practices.
Deploy & Operate Models in Production - Deploy models to production (batch and/or real-time) with attention to latency, reliability, and cost
- Implement MLOps pipelines covering training, testing, versioning, deployment and model lifecycle management.
- Monitor production models for model performance, data quality, drift and other operational issues.
- Implement appropriate retraining, rollback and model versioning strategies.
- Troubleshoot issues across data pipelines, models, inference services, APIs, and production environments.
- 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)
The Coca-Cola Company will not offer sponsorship for employment status (including, but not limited to, H1-B visa status and other employment-based nonimmigrant visas) for this position. Accordingly, 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.
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: 171,000 - 198,000 USD
Base 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:30
Annual 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 America
City/Cities:Atlanta
Travel Required:00% - 25%
Relocation Provided:Yes
Job Posting End Date:October 14, 2026
