Data Scientist Ml Ai Python

JOB DESCRIPTION Key Responsibilities
• Build, train, and deploy machine learning models for predictive analytics, optimization, and business intelligence use cases.

Perform Exploratory Data Analysis (EDA) to uncover patterns, trends, and actionable insights from structured and large-
scale datasets.

• Develop feature engineering pipelines and feature stores to enable scalable and reusable ML workflows.
• Design end-to-end data science solutions covering data preparation, model development, validation, and deployment.
• Implement MLOps practices to manage model lifecycle including versioning, monitoring, and continuous improvement.
• Work with Databricks and Spark environments for large-scale data analysis and model development.
• Deploy and manage ML models using GCP services such as Vertex AI or similar cloud ML platforms.

Collaborate with business stakeholders, data engineers, and analytics teams to translate business problems into AI-driven
solutions.
• Evaluate and improve model performance using experimentation, hyperparameter tuning, and validation techniques.
Core Skills Required

• Strong hands-on experience in Machine Learning, Statistical Modeling, and Predictive Analytics.
• Proficiency in Python (Pandas, NumPy, Scikit-learn) for data analysis and model development.
• Experience working with Databricks (DBx) and Spark / PySpark environments.
• Hands-on experience with MLOps frameworks and model lifecycle management.
• Experience deploying or managing ML models on GCP (Vertex AI) or similar cloud ML platforms.
• Strong knowledge of Feature Engineering, Feature Store concepts, and ML experimentation.
• Expertise in EDA, model evaluation, and performance optimization techniques.

Good to Have

• Experience with MLflow, Databricks Feature Store, or model monitoring tools.
• Exposure to CI/CD pipelines for ML deployment.
• Experience working with large-scale distributed datasets.
• Domain knowledge in BFSI, fraud analytics, risk modeling, or customer analytics.

EXPERTISE AND QUALIFICATIONS

Key Responsibilities
• Build, train, and deploy machine learning models for predictive analytics, optimization, and business intelligence use cases.

Perform Exploratory Data Analysis (EDA) to uncover patterns, trends, and actionable insights from structured and large-
scale datasets.

• Develop feature engineering pipelines and feature stores to enable scalable and reusable ML workflows.
• Design end-to-end data science solutions covering data preparation, model development, validation, and deployment.
• Implement MLOps practices to manage model lifecycle including versioning, monitoring, and continuous improvement.
• Work with Databricks and Spark environments for large-scale data analysis and model development.
• Deploy and manage ML models using GCP services such as Vertex AI or similar cloud ML platforms.

Collaborate with business stakeholders, data engineers, and analytics teams to translate business problems into AI-driven
solutions.

• Evaluate and improve model performance using experimentation, hyperparameter tuning, and validation techniques.
Core Skills Required

• Strong hands-on experience in Machine Learning, Statistical Modeling, and Predictive Analytics.
• Proficiency in Python (Pandas, NumPy, Scikit-learn) for data analysis and model development.
• Experience working with Databricks (DBx) and Spark / PySpark environments.
• Hands-on experience with MLOps frameworks and model lifecycle management.
• Experience deploying or managing ML models on GCP (Vertex AI) or similar cloud ML platforms.
• Strong knowledge of Feature Engineering, Feature Store concepts, and ML experimentation.
• Expertise in EDA, model evaluation, and performance optimization techniques.

Good to Have

• Experience with MLflow, Databricks Feature Store, or model monitoring tools.
• Exposure to CI/CD pipelines for ML deployment.
• Experience working with large-scale distributed datasets.
• Domain knowledge in BFSI, fraud analytics, risk modeling, or customer analytics.