Digital products playa central rolein 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 acrossCoca-Cola's North America Operating Unit.
Our work spans customer journeys, service delivery, sales workflows, and the platforms thatconnectthem. 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'llpartner 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 WillWork 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 primaryobjectiveof 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 WorkYou'llbe 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 approachesappropriate tothe product problem (e.g., gradient boosting, deep learning, ranking)
- Run experiments and evaluate models using soundmethodology(train/validation splits, cross-validation asappropriate, 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 &OperateModels 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 behaviorimpactscustomers 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) andMLOpstooling (feature stores, training infrastructure, deployment, monitoring)
WhatWe'reLooking 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/lakehouseconcepts), 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 withlakehouse/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 ForThis 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)
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