- Company Name
- Restaurant Brands International
- Job Title
- AI/ML Data Scientist
- Job Description
-
**Job Title**
AI/ML Data Scientist
**Role Summary**
Design, build, and operationalize scalable enterprise forecasting and machine learning pipelines. Own end‑to‑end data pipelines, feature engineering, and evaluation frameworks that support sales, labor, inventory, and other predictive domains. Enable semantic and AI layers for natural language access to insights while ensuring governance, explainability, and data lineage.
**Expectations**
- Deliver production‑grade, reproducible ML systems that integrate cleanly with the data ecosystem.
- Maintain rigorous version control, experimentation tracking, and automated drift detection.
- Collaborate with cross‑functional teams to translate business requirements into ML‑ready data assets.
- Adhere to enterprise policies for data governance, RBAC, and auditability.
**Key Responsibilities**
- **Forecasting & ML Engineering** – Design and operationalize forecasting pipelines and feature layers; build reusable ML components; implement best practices for time‑series modeling.
- **Data & Platform Engineering** – Onboard, transform, and model enterprise data in Snowflake; design high‑performance batch and real‑time pipelines; optimize data structures for scalability and security.
- **Semantic & AI Layer Enablement** – Build semantic models and metadata to support NLP-driven insight retrieval; enable AI tools with clean, governed datasets.
- **Governance, Quality, & Trust** – Enforce RBAC, documentation, and data quality checks; develop evaluation datasets, regression tests, and drift detection mechanisms.
- **Cross‑Functional Collaboration** – Partner with Finance, Operations, Marketing, and Product to embed forecasts and predictive insights into planning workflows; advocate for best practices in data and model architecture.
**Required Skills**
- 5+ years in data engineering or analytics engineering with production experience on Snowflake or equivalent cloud warehouses.
- Expert SQL and advanced data modeling (star/snowflake schemas, semantic modeling, metadata management).
- Proficient in Python; experience operationalizing ML workloads (model serving, versioning, testing).
- Knowledge of ML engineering best practices: experiment tracking, feature stores, drift detection, version control.
- Strong understanding of data governance, RBAC, audit trails, and handling of PII.
- Excellent communication and collaboration across multidisciplinary teams.
**Required Education & Certifications**
- Bachelor’s degree (or higher) in Computer Science, Data Engineering, Statistics, Applied Mathematics, or a related field.
- Relevant certifications in big data platforms (Snowflake, AWS, GCP) or ML operations (e.g., Certified Data Engineer, ML Ops) are advantageous.