Data Scientist 3 - Machine learning & Python

We are looking for a Data Science engineer who can independently work on understanding & scoping business problems , apply strong analytical thinking, and build production-ready solutions that move from insights to predictive modeling and actionable outcomes.

Key Responsibilities

• Work with product, business, and engineering teams to understand problem statements, clarify objectives, and translate them into structured analytical tasks.
• Perform data analysis to identify trends, drivers, anomalies, and root causes, explaining both what is happening and why.
• Build, evaluate, and refine machine learning models (classification, regression, anomaly detection) 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.
• Design and develop MLbacked APIs, including load handling, pagination and performance considerations.
• Collaborate with data engineering and platform teams to ensure data quality, reliability and scalability of ML solutions.

Required Skills & Experience

• Strong handson experience in Python and applied Data Science / Machine Learning.
• Practical experience moving from EDA and insight generation to predictive modeling and operational use.
• Working knowledge of ML Ops, including model deployment, monitoring, and CI/CD pipelines.
• Understanding of data structures & algorithms (DSA) relevant to efficient data processing and APIdriven systems.
• Experience in developing with analytics/ML services APIs

Good to Have

• Exposure to Data Engineering concepts such as Spark fundamentals and distributed computing.
• Strong working knowledge of SQL for analysis, debugging, and data validation.

Role Expectations

• Own individual DS/ML components endtoend and associated systemlevel design.
• Clearly articulate insights, assumptions and limitations to both technical and nontechnical stakeholders.
• Focus on impact, correctness and maintainability.

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