Data Science - ML

Job Description

Data Scientist (End-to-End Machine Learning Lifecycle)

We are looking for a Data Scientist with strong end-to-end ownership across the entire machine learning lifecycle - spanning problem framing, data exploration, feature engineering, model development, experimentation, deployment and monitoring.

Key Responsibilities

1. Problem Framing & Business Understanding

2. Data Exploration & Feature Engineering

3. Model Development

• Expertise across regression, tree-based methods (XGBoost, LightGBM), clustering, embeddings, deep learning, and NLP techniques.

4. Deployment & ML Engineering Collaboration

• Work closely with ML engineers to deploy models using modern MLOps tools (Vertex AI, AWS Sagemaker, GCP, Kubeflow, Airflow/Composer, Git/GitLab).

Required Skills & Experience

Technical Skills

• Strong Python (Pandas, NumPy, Scikit-Learn; PySpark preferred) and SQL.
• Experience building and deploying ML models end-to-end in production.
• Familiarity with cloud ML platforms (GCP, AWS, Azure), especially pipelines and orchestration.
• Experience with version control (Git), CI/CD, Docker, and modern data platforms (BigQuery, Snowflake, Redshift, Databricks).