Lead model monitoring activities, including tracking performance metrics, detecting model and data drift, identifying data quality issues, providing root cause analysis, and recommending remediation strategies.
Conduct rigorous model validation by providing effective challenges during model development phases, including performance testing, benchmarking, provide remediation plan, and documentation to ensure models meet business, technical, and regulatory standards.
Explore and aggregate data independently to uncover data anomalies that impact algorithm performance
Write production level code in a dynamic, start-up environment
Solve complex problems using terabyte size data sets
Apply of a variety of machine learning techniques to a business problem to arrive at optimal approach
Partner with Product and Engineering teams to solve problems and identify trends and opportunities
Explain and visualize results and algorithm performance to non-technical audiences
Support the company's commitment to protect the integrity and confidentiality of systems and data.

Master’s Degree in Mathematics, Statistics, Computer Science, Operational Research or related field
A minimum of 2 years of data science, engineering, mathematics, or related work experience
Experience developing data science pipelines & workflows in Python, R or equivalent programming language
Experience in writing and tuning SQL
Experience handling terabyte size datasets with Spark language
Experience applying various machine learning techniques, and understanding the key parameters that affect model performance
Experience using ML libraries, such as scikit-learn, mllib, etc.
Experience using data visualization tools
Able to write production level code, which is well-written and explainable
Ability to effectively communicate findings from complex analyses to non-technical audiences.

Data Scientist II – Contract

Key skills