Data Scientists

Design build and optimize predictive models and machine learning algorithms using structured and semi structured data preprocessing feature engineering and model selection independently.

Build and maintain automated model pipelines including training validation scoring and model drift detection retraining logic and performance code-based model explainability support documentation for governance review.

Advanced proficiency in Python (Pandas Num Py Scikit-learn XGBoost Light

GBM).

Strong command of SQL and handling large datasets.

Experience deploying models using MLflow Airflow Docker or similar tools.

Familiarity with model performance metrics.

Hands on in training and evaluating models for binary classification multi class regression or time series.

Exposure to deep learning for advanced use cases.

Working knowledge of embeddings vector stores or text-based models.

Git-based versioning and reproducible ML workflow setup.

• Apache Hive
• S3
• Hadoop
• Redshift
• Spark
• AWS
• Apache Pig
• No
  
  SQL
• Big Data
• Data Warehouse
• Kafka
• Scala