Build, deploy, and optimize ML models for predictive analytics, forecasting, classification, and regression
Perform large-scale feature engineering using PySpark and Big Data tools
Work on batch pipelines, model versioning, and experiment tracking
Develop cost estimation and risk/likelihood models using statistical and ML techniques
Build NLP pipelines using deep learning frameworks such as PyTorch, TensorFlow, or similar
Develop real‑time, low‑latency inference systems for text classification, embeddings, semantic search, summarization, and retrieval
Create prompts, context graphs, and agentic workflows for LLM-based systems
Apply knowledge of prompt engineering, context engineering, and autonomous agent frameworks to production systems
Work in Databricks for ETL, feature engineering, ML training, and orchestration
Use Azure services for model deployment, data pipelines, and infrastructure
Collaborate using Git-based workflows; leverage tools like GitHub Copilot, Claude Code, etc.
Implement model monitoring, observability, drift detection, and performance tracking

3–7 years of experience as a Data Scientist
Strong hands-on experience with Databricks (Delta Lake, MLflow, Job Orchestration)
Excellent PySpark skills for large-scale distributed data processing
Proficiency in Azure cloud services (ADF, Azure ML, AKS, Databricks on Azure)
Strong understanding of ML algorithms, statistical methods, and data analysis
Experience with deep learning frameworks: PyTorch, TensorFlow, Transformers (HuggingFace)
Experience with model monitoring and ML observability
Knowledge of LLM-based agentic frameworks (LangChain, Semantic Kernel, CrewAI, AutoGen, etc.)
Good to Have: Experience with containerization (Docker, Kubernetes, AKS)
Good to Have: Experience deploying models to production (REST APIs, real-time endpoints)

Data Scientist

Key skills