Key Responsibilities:

Data Science:

• Design, develop, and deploy machine learning models to solve business problems.

• Perform data exploration, cleaning, and preprocessing to prepare datasets for modeling.

• Conduct feature engineering to extract meaningful features from raw data.

• Implement and evaluate machine learning algorithms and techniques.

• Analyze model performance and optimize models for accuracy, efficiency, and scalability.

• Collaborate with cross-functional teams to understand business requirements and translate

them into data-driven solutions.

MLOps:

• Develop and maintain CI/CD pipelines for model training, validation, and deployment.

• Monitor and manage machine learning models in production to ensure reliability and

performance. Implement best practices for version control, model reproducibility, and

experiment tracking. Automate workflows for data ingestion, model training, and deployment.

• Ensure compliance with data privacy and security standards.

• Troubleshoot and resolve issues related to model performance and infrastructure.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a

related field.

• 5+ years of experience in data science, machine learning, and/or MLOps.

• Proficiency in programming languages such as Python and experience with machine learning

libraries (e.g., scikit-learn, TensorFlow, PyTorch).

• Experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, DVC).

• Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies

(e.g., Docker, Kubernetes).

• Strong understanding of data engineering principles and experience with ETL processes.

• Excellent problem-solving skills and the ability to work independently and as part of a team

Sr. Lead ML Ops