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Key job responsibilities
**Predictive Modeling:**
* Develop predictive models to identify potentially fraudulent streaming activities, or content uploads. Utilize anomaly detection techniques to surface suspicious patterns in user behavior, and content metadata
* Collaborate with the FCRM team to refine fraud detection models and improve the accuracy of fraud identification in addition to improving streaming fraud entitlement estimation by using ML output to identify outliers
* Develop models to automate metadata enrichment and quality assurance processes
* Develop models to determine drivers of key performance metrics like MAU and HPC and automate the process of deep diving into variances in these metrics
**Experimentation and A/B Testing:*** Analyze the results of these experiments, identify learning, and provide recommendations to optimize the solutions
* Partner with Sr. Data Scientists to analyze and propose key experimentation success and guardrail metrics for business like AMOR, Catalog quality etc.
**Deep dives and analysis:**
* Develop metrics and rules that help identify metadata tag defects and improve metadata quality and enable more precise Catalog tiering
* Analyze the current state of metadata, including genre coverage, artist disambiguation, and track-level information and propose strategies to improve the overall metadata quality and coverage across the catalog
* Determine relationship between metadata quality improvements and customer engagement to guide prioritization of ingestion and coverage
**Cross - Functional Collaboration:**
* Partner with product, catalog, and engineering teams to understand business challenges and translate them into analytical science initiatives
* Evangelize the use of experimentation and advanced analytics science across the organization
* Mentor and train other analysts and data scientists to build their skills
- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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