Description :

Job Summary :

You will be responsible for leveraging large datasets to build, train, and deploy machine learning models that drive key business decisions and product features.

The ideal candidate has a strong statistical background and practical experience with MLOps.

Key Responsibilities :

- Model Development: Design, build, and evaluate predictive and prescriptive models (e.g., classification, regression, clustering) using Python and libraries like Scikit-learn and TensorFlow/PyTorch.

- Data Engineering: Collaborate with Data Engineers to clean, transform, and manage data from various sources using SQL and distributed processing frameworks like Spark.

- Deployment: Implement and maintain ML pipelines in a production environment using MLFlow or other MLOps tools.

- Analysis & Insights: Perform ad-hoc analysis and data visualization to communicate findings and recommendations to non-technical stakeholders.

Required Technical Skills :

- Programming: High proficiency in Python (Pandas, NumPy, Scikit-learn).

- ML Frameworks: Deep expertise in TensorFlow or PyTorch.

- Data & Tools: Strong SQL skills.

- Experience with Spark (PySpark) and cloud data platforms (AWS Sagemaker, Azure ML, GCP AI Platform).

- Statistical Methods: Solid understanding of statistical modeling, experimental design, and A/B testing.

- Version Control: Git, MLOps practices