Senior Data Scientist

Requirements:

• 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.