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

• 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, Tensor Flow, XGBoost, Hugging Face Transformers).
• Experience with MLOps tools (e.g., MLflow, Sage Maker) 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, Git Ops, 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 Lang Chain, Lang Graph, or Llama Index.