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
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)
Requirements
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 :
Additional Information
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 , e nabling 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.
Coca-cola · Us, GA