Senior Data Scientist / Senior ML Engineer

You will lead the design, development, and deployment of ML/AI/GenAI models that power core Foodics products (e.g., pricing, personalization, fraud detection). You ll collaborate with Data Engineers, Product Managers, and Platform teams to deliver production-grade models with real impact. What Will You Do Own end-to-end ML model lifecycle: problem framing, data exploration, training, deployment, monitoring. Design and develop scalable solutions using classical ML and GenAI techniques. Implement MLOps best practices: versioning, reproducibility, monitoring, CI/CD for models. Collaborate with squads and platform teams to ensure reusability and adherence to standards. Mentor junior ML engineers and contribute to the internal ML knowledge base. Integrate models with APIs and backend services as needed. Embrace and enforce a "you build it, you run it" approach, owning the full lifecycle of ML models from development through monitoring and continuous improvement.

• 5+ years experience in applied ML, AI, or data science.
• Strong proficiency in Python and ML/AI libraries (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, HuggingFace Transformers).
• Experience with MLOps tools (e.g., MLflow, SageMaker) and managing versioning, testing, and observability.
• Deep understanding of model development workflows including feature engineering, hyperparameter tuning, model evaluation, and A/B testing.
• Deep understanding of statistical modeling, statistical inference, and the appropriate application of statistical tests (e.g., t-test, chi-square, ANOVA, regression analysis); ability to interpret results and communicate implications to both technical and non-technical audiences.
• Proven track record of deploying ML models in production at scale.
• Knowledge of ML best practices including bias mitigation, explainability (e.g., SHAP, LIME), and model monitoring for drift and fairness.
• Strong understanding of data pipelines, experimentation, and model evaluation.
• Familiarity working in a cloud-native environment (AWS preferred) with CI/CD, GitOps, and IaC tools (e.g., Terraform, CDK).
• Hands-on experience with GenAI / LLM integration (e.g., RAG, fine-tuning, embeddings, prompt engineering) and tools such as LangChain, LangGraph, or LlamaIndex.