Description

Design, build, and deploy machine learning models and end-to-end data science pipelines for production environments.

This role is on-site.

Responsibilities

• Develop and deploy predictive analytics, classification, and recommendation systems using Python and deep learning frameworks.
• Build scalable data ingestion, feature engineering, and training pipelines using Spark, PySpark, and SQL.
• Implement MLOps practices including CI/CD, Docker, Kubernetes, and model monitoring for drift detection.
• Conduct statistical analysis, hypothesis testing, and A/B testing to validate model performance.
• Create visualizations and dashboards to communicate insights to stakeholders and lead technical initiatives.

Required Skills

• 10+ years of experience in data science and machine learning development.
• Advanced proficiency in Python (NumPy, Pandas, Scikit-learn, XGBoost, LightGBM).
• Strong experience with deep learning frameworks such as TensorFlow or PyTorch.
• Hands-on expertise with big data technologies including Spark, PySpark, and Hadoop ecosystems.
• Advanced SQL skills and experience with enterprise RDBMS platforms.
• Proven experience deploying models using Docker, Kubernetes, and CI/CD pipelines.
• Experience with cloud platforms (Azure, AWS, or GCP) for scalable model training and deployment.
• Strong foundation in statistics, probability, linear algebra, and predictive modeling.

Preferred Skills

• Experience with Natural Language Processing (NLP) and text analytics.
• Proficiency in data visualization tools like Power BI, Tableau, or Plotly.

Key Skills

Education

Any Gradute