Data Scientist

Job Summary

Data Scientist

individual contributor

Databricks

PySpark

SQL

Key Responsibilities

• Own and deliver the complete lifecycle of machine learning projects, from data exploration and feature engineering to model deployment and monitoring.
• Develop, optimize, and maintain ML and DL models using scalable tools and frameworks.
• Build and maintain robust MLOps pipelines for model versioning, testing, deployment, and retraining.
• Work extensively on Databricks, leveraging PySpark, MLflow, and SQL to build production-grade ML pipelines.
• Automate and monitor workflows for data preparation, model training, and model performance tracking.
• Integrate ML solutions with business systems and APIs for real-time or batch inference.
• Collaborate with data engineers, product managers, and domain experts to translate business problems into ML solutions.
  

Qualifications & Skills

• Bachelor’s or master’s degree in Computer Science, Data Science, or a related field from top-tier institutions.
• 2+ years of experience as an ML Engineer, Data Scientist, or in a similar technical role.
• Demonstrated experience deploying at least one end-to-end ML pipeline into production.
• Strong command of Databricks, PySpark, and SQL for large-scale data processing.
• Proficiency with Python and familiarity with MLOps tools like MLflow, Airflow, or Kubeflow.
• Hands-on experience with cloud platforms (Azure preferred; AWS or GCP acceptable).
• Solid understanding of machine learning and deep learning techniques, as well as EDA and data wrangling.
• Familiarity with CI/CD, Docker, Kubernetes, and version control (Git).
• Self-driven, highly organized, and capable of working independently with minimal supervision.