Senior Data Scientist

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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Job Description

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