Senior Data/Machine Learning Engineer

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.   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/retraini