Senior Data Scientist / Senior ML Engineer
Summary
Build, deploy, and own ML/AI models (including GenAI) for pricing, personalization, and fraud detection, using Python, PyTorch/TensorFlow, and MLOps tools in a cloud-native stack.
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
Experience / Requirements
- 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.