Job Description

Senior Data Scientist / Data Engineer (8-12 years) to drive end-to-end analytics, data engineering pipelines, and cloud-based ML solutions. This role will design, build, and operationalize scalable data platforms and analytical models to support business decision-making.

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Position Summary

The role involves developing advanced ML models, building robust data pipelines, and architecting cloud-based data solutions. The incumbent will collaborate with cross-functional teams to deliver high-impact analytical insights and scalable data products.

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Key Responsibilities:

Design, develop, and deploy end-to-end ML models and analytical frameworks for business use cases. Build and maintain scalable ETL/ELT pipelines using modern data engineering tools such as Spark, Airflow, and cloud-native services. Architect and optimize data lake/data warehouse solutions for performance, reliability, and scalability. Perform advanced analytics, feature engineering, model tuning, and performance monitoring. Implement MLOps practices for model versioning, CI/CD, and production monitoring. Work with structured, semi-structured, and unstructured data to deliver high-quality datasets for analytics. Collaborate with business stakeholders, product owners, and engineering teams to translate business requirements into technical solutions. Mentor junior team members and contribute to standardization, best practices, and knowledge sharing.
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Requirements / Qualifications:

Education:

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related field. Master's degree preferred.

Experience & Requirements:

8-12 years of hands-on experience across Data Science and Data Engineering domains. Strong proficiency in Python, SQL, PySpark/Spark, and distributed data processing. Expertise in ML modelling--classification, regression, clustering, NLP, time series, and model deployment. Experience building data pipelines and working with tools like Airflow, Kafka, DBT, Hadoop ecosystem. Proven experience with cloud platforms (AWS/Azure/GCP) and cloud-native data/ML services. Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI. Strong communication, analytical thinking, and stakeholder management skills. Experience working with large-scale datasets and real-time/near-real-time data solutions.
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Requirements / Qualifications:

Strong problem-solving mindset with the ability to convert business problems into analytical solutions. Ability to work in a fast-paced, collaborative environment. Experience with Agile methodologies and cross-functional team collaboration. Knowledge of BI tools (Power BI, Tableau) is a plus. Industry experience in BFSI/Retail/Healthcare preferred.
Job Type: Full-time

Benefits:

Work from home
Work Location: Remote