Hiring For Data Scientist Role || Coforge

Key Responsibilities:

- Lead the design, development, and deployment of machine learning models across various business domains.

- Architect and implement end-to-end ML pipelines using AWS Sagemaker AI, MLflow for experiment tracking, model versioning, and lifecycle management.

- Oversee model training, tuning, and deployment using Amazon SageMaker AI, including Pipelines, Model Registry, and Model Monitor.

- Mentor and guide a team of data scientists, ensuring best practices in coding, experimentation, and documentation.

- Collaborate with engineering, product, and business teams to translate complex problems into data-driven solutions.

- Establish and enforce MLOps standards for reproducibility, scalability, and monitoring of ML models in production.

- Drive innovation by evaluating and integrating emerging tools and technologies in the ML ecosystem.

- Present findings and insights to senior leadership and stakeholders in a clear and actionable manner.

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Required Qualifications:

- 4+ years of experience in data science, with at least 2 years in a leadership or mentoring role.

- Proven expertise in MLflow and Amazon SageMaker for model management and deployment.

- Strong programming skills in Python and experience with ML libraries such as scikit-learn, TensorFlow, PyTorch.

- Deep understanding of machine learning algorithms, feature engineering, and model evaluation techniques.

- Experience with cloud platforms (preferably AWS) and containerization tools like Docker.

- Familiarity with CI/CD pipelines, data versioning, and monitoring tools.

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Preferred Skills:

- Experience with SageMaker Studio, SageMaker Pipelines, and SageMaker Clarify.

- Knowledge of feature stores, data governance, and responsible AI practices.

- Strong communication and leadership skills with the ability to influence across teams.

- Experience in deploying ML solutions in regulated industries (e.g., finance, healthcare) is a plus.