- 5+ years of Experience : A minimum of 5 years of professional experience as a Data Scientist, with a proven track record of developing and deploying machine learning models in a production environment.

- Proficiency in Python : Extensive experience in developing and deploying scalable production-level code in Python, with a deep understanding of machine learning libraries such as Scikit-learn, PyTorch, TensorFlow, or similar.

- Database Expertise : Strong experience working with relational databases, writing optimized SQL queries, and managing large datasets.

- End-to-End ML Lifecycle : Comprehensive experience in the full lifecycle of ML model development, including ideation, training/testing, deployment, and monitoring in a production setting.

- Data Analysis & Insight Generation : Ability to analyze complex datasets and extract actionable insights that inform business decisions.

- Educational Background : A Master's or Ph. D. in Statistics, Computer Science, Engineering, or a related quantitative discipline.

- Communication Skills : Proven ability to communicate complex technical concepts and findings to non-technical stakeholders, with a focus on clear and actionable insights.

Bonus Points :

- Cross-Functional Experience : Experience working cross-functionally with Data/MLOps Engineering, Analytics, Business, and Product teams.

- Domain Expertise : Previous experience in specialized areas such as fraud detection, credit risk prediction, or graph models and analytics.

- MLOps & Cloud Experience : Hands-on experience with MLOps, particularly within the AWS ecosystem, and familiarity with tools like Apache Airflow.

- Experimental Design : Expertise in designing and executing experiments to measure model impact and effectiveness.