(Senior) Partner Decision Science Associate
What the job entails
- Be the critical bridge between banking / fintech partners and the Credit Karma data science platform. Help some of the biggest financial institutions in the country work with the CK modeling platform.
- Work hand-in-hand both with the internal data science teams, as well as partner risk science teams to integrate and build decision science models on the CK platform.
- Build strong relations with data science teams at financial institutions, ranging from VP-level executives all the way to hands-on data scientists.
- Thoroughly understand the capabilities of the Credit Karma machine learning platform, and help drive requirements as well as directly enhance Karma’s capabilities.
- Optimize and guide the partner model building and analytics process, in order to achieve optimal results. Coach partner risk teams on machine learning capabilities provided by the CK platform, while getting in their shoes and speaking their language.
Our ideal candidate
- Advanced Degree (Ph.D./MS) in Statistics, Math, Engineering or a related quantitative discipline.
- 5+ years of experience in statistical analysis and modeling especially underwriting, customer segmentation, customer valuations.
- 3+ years of experience building and implementing complex models in a financial risk / data science environment. Experience with consumer credit data a must-have.
- Expert knowledge of Python (or R or SAS), and SQL, or similar industry standard tools used for large-scale data analysis and modeling.
- Experience and/or interest in latest machine learning techniques (Random Forest, GBT, Deep Learning) and tools (Python, SciKit-Learn, Hadoop, Hive, etc) strongly preferred.
- Experience interfacing with teams at Financial institutions strongly preferred.
- Self-motivated, results oriented, enthusiastic, and a creative thinker.
Meet Some of Credit Karma's Employees
Kyle works behind the scenes as a revenue analyst to provide Credit Karma’s members with personalized offers that help them optimize their finances.
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