Overview
Blue Yonder is looking for a hands-on Machine Learning Engineer / Data Scientist (3–4 years of experience) to design, build, and deploy ML solutions that power intelligent decision-making across our supply chain and retail platforms. The primary focus is on production ML — forecasting, optimization, recommendations, and anomaly detection on structured tabular datasets.
As a secondary responsibility, the role will contribute to Generative AI initiatives (LLM-powered features, RAG pipelines, intelligent assistants) that complement our classical ML stack. This is not a research role — we are looking for someone who has shipped ML systems to production and can own problems end-to-end.
What You'll Do
- Design, develop, and deploy ML models for forecasting, classification, regression, recommendations, and anomaly detection use cases across supply chain and retail.
- Build and maintain end-to-end ML pipelines — feature engineering, training, evaluation, inference, and monitoring — using modern MLOps tooling.
- Translate ambiguous business problems into well-defined ML problems with clear success metrics.
- Design and analyze experiments (A/B tests, offline evaluations) to quantify model and business impact.
- Monitor models in production for drift and performance degradation, drive retraining cycles.
- Contribute to GenAI features — RAG pipelines, LLM-powered assistants, summarization/extraction — using prompt engineering, retrieval strategies, and evaluation frameworks (groundedness, faithfulness, hallucination rate).
- Partner with product, data engineering, and domain experts to take ideas from POC to production.
Required Skills
What we are looking for:
- Python-Pandas, scikit-learn, NumPy, production-quality scripting
- Machine Learning-Classification, regression, clustering, anomaly detection
- SQL-Querying and working with relational databases
- Model Evaluation-Cross-validation, metrics, bias/variance tradeoff
- Data Wrangling-Handling real-world messy tabular data
- Big Data: Spark / PySpark, Snowflake / BigQuery, or similar; feature engineering at scale.
- Cloud & MLOps: Hands-on with at least one cloud platform (Azure / AWS / GCP); experience with MLflow, Airflow, SageMaker, Vertex AI, or Azure ML.
- Deployment: REST APIs, batch pipelines, or real-time inference; familiarity with Docker and basic Kubernetes.
- DevOps: Git, GitHub (Actions, CI/CD), branching strategies, and code-review discipline.
- LLMs & RAG: Working knowledge of LLM APIs (Azure OpenAI, OpenAI, Anthropic), RAG architectures, embeddings, and vector databases (FAISS, Pinecone, Azure AI Search, pgvector).
- Frameworks: Exposure to LangChain, LangGraph, or LlamaIndex.
- Evaluation: Understanding of GenAI evaluation metrics; experience with Langfuse or Ragas is a plus.
Good To Have
- Advanced ML: XGBoost, LightGBM, Isolation Forest, or ensemble methods.
- Time Series: ARIMA, Prophet, or modern deep-learning forecasting approaches.
- MLOps depth: MLflow for experiment tracking and model lifecycle management.
- GenAI delivery: Shipped at least one RAG or LLM-powered feature to production.
Our Values
If you want to know the heart of a company, take a look at their values. Ours unite us. They are what drive our success – and the success of our customers. Does your heart beat like ours Find out here: Core Values
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
