Data Scientist 3 - Machine learning, Data Engineering & 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, with a strong foundation in data engineering and scalable data systems. 
 

Key Responsibilities 

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

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

• 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. 

• 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 and maintain ML models in production, following best practices for reliability and lifecycle management (MLOps exposure 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. 

• Proficiency in 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 predictive modeling and 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 of MLOps, 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.