Data Scientist 3 - Machine learning, Data Engineering & Python

We are looking for aData Science engineerwho can independently work onunderstanding & scopingbusiness problems, apply strong analytical thinking, and buildproduction-ready solutionsthat move from insights to predictivemodelingand actionable outcomes,witha strong foundationin data engineering and scalable data systems.

Key Responsibilities

• Work with product, business, and engineering teams to understand problem statements, clarifyobjectives, and translate them into structured analytical and data engineering tasks.

• Design, build, andmaintainrobust data pipelines to ingest, process, and transform large-scale structured and unstructured data for downstream analytics and ML use cases.

• Perform data analysis toidentifytrends, 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.

• Develop and manage feature pipelines and data workflows that ensure consistency, reliability, and scalability of ML inputs.

• Design and develop ML-backed APIs, including load handling, pagination, and performance considerations.

• Collaborate closely with data engineering and platform teams to ensure data quality, governance, reliability, and scalability of end-to-end solutions.

• Deploy andmaintainML models in production, following best practices for reliability and lifecycle management (MLOpsexposure is a plus).

Required Skills & Experience

• Strong hands-on experience in Python and applied Data Science / Machine Learning.

• Solid experience in Data Engineering, including building scalable data pipelines and working with large datasets.

• Proficiencyin SQL for data extraction, transformation, analysis, and debugging.

• Experience with distributed data processing frameworks (e.g., Spark) and understanding of data architectures.

• Practical experience moving from EDA and insight generation to predictivemodelingand operational use.

• Understanding of data structures & algorithms (DSA) relevant to efficient data processing and API-driven systems.

• Experience in developing with analytics/ML services APIs.

Good to Have

• Working knowledge ofMLOps, including model deployment, monitoring, versioning, and CI/CD pipelines.

• Experience with cloud-based data platforms or modern data stack tools.

• Familiarity with data governance, data quality frameworks, and observability tools.

Role Expectations

• Own individual DS/ML and data engineering components end-to-end, including system-level design and data workflows.

• Clearly articulate insights, assumptions, and limitations to both technical and non-technical stakeholders.

• Focus on impact, correctness, scalability, and maintainability of solutions.