Strong proficiency in python scikit-learn pandas numpy
Experience building and deploying regression and classification models
The role involves owning the full modeling lifecycle from problem framing through model development, validation, and deployment with a focus on supervised learning applications
Job Summary
The role involves owning the full modeling lifecycle from problem framing through model development, validation, and deployment with a focus on supervised learning applications.
Candidates will collaborate with business stakeholders, data engineers, and product teams to transform raw data into actionable insights and production-ready machine learning solutions.
The position requires translating complex analytical findings into clear narratives for non-technical audiences while contributing to team growth through code reviews and knowledge sharing.
Matching Summary
The role involves owning the full modeling lifecycle from problem framing through model development, validation, and deployment with a focus on supervised learning applications.
Skills & Requirements
Must-have
3 to 5 years hands-on data science experience
Strong proficiency in Python scikit-learn pandas NumPy
Experience building and deploying regression and classification models
Solid understanding of statistical fundamentals and hypothesis testing
Experience working with SQL for data extraction
Nice-to-have
Experience with Domino Lab platform
Familiarity with MLflow Kubeflow experiment tracking tools
Background in financial services or regulated industries
Exposure to imbalanced classification problems and SMOTE techniques
Experience with time series regression or survival analysis
Key Requirements
Bachelor's or advanced degree in Statistics Mathematics Computer Science Data Science
3 to 5 years of relevant quantitative role experience