Work with product, business, and engineering teams to understand problem statements, clarify objectives, and translate them into structured analytical tasks
Job Summary
Work with product, business, and engineering teams to understand problem statements, clarify objectives, and translate them into structured analytical tasks.
Build, evaluate, and refine machine learning models with strong focus on feature engineering and validation.
Deploy and maintain ML models in production, following ML Ops best practices such as versioning, CI/CD integration, monitoring, and retraining.
Matching Summary
Work with product, business, and engineering teams to understand problem statements, clarify objectives, and translate them into structured analytical tasks.
Skills & Requirements
Must-have
Python and applied Data Science
Machine Learning model development
ML Ops best practices
ML backed APIs development
data structures & algorithms
Nice-to-have
Spark fundamentals and distributed computing
SQL for analysis and debugging
strong analytical thinking
focus on impact, correctness and maintainability
Key Requirements
Strong hands on experience in Python
Practical experience moving from EDA to predictive modeling
Working knowledge of ML Ops
Experience in developing with analytics/ML services APIs