Lead Senior Data Scientist Developer Productivity at Salesforce

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.Job CategoryProducts and TechnologyJob DetailsThe Developer Productivity Experience (DPX) team owns the building of all production artifacts from 10s of millions of lines of source code.  In doing so, hundreds of thousands of VMs are spun up each day to build and test features and do final validation of the code before it is run in production.  At Salesforce scale, it is not just about checking everything but checking the right things at the right time to provide confidence in our software.   Our mission is to make developers more productive with a reliable system and actionable data. Examples of the questions we are trying to answer include:
You have 1.2 million tests and you only have time to run 1% of them in 1 hour. Which ones do you choose? 
Using data on test executions, the source code, production stack traces, and how developers respond to failures (e.g. which test failure do they look at first), how do you assess the value of the test?
A complex piece of software is modified by hundreds of teams simultaneously, how can you assign a numerical value to the risk of a particular change?
Looking at production stack traces, how do you identify patterns and associate them with the source code?
You have hundreds of microservices that depend on each other and an integration test is failing. How do you create and assign bugs to the right team?
A Data Scientist with our team must possess both the AI/ML skills to improve the performance of our classifiers and recommender systems, as well as engineering skills to build and deploy a production-quality service. The ideal candidate:
Has in-depth knowledge of Artificial Intelligence and Machine Learning
Has a strong grasp of the foundational ML principles
Has experience coding in Java
Has experience coding in Python 
Analyzes data using Jupyter notebooks
Core Qualifications
BS in Computer Science or a related field such as Applied Mathematics, Neuroscience, Electrical Engineering, Physics, etc.
AWS/SageMaker or GCP cloud experience
Either Java and/or Python proficient
Experience with Agile development methodology
Experience with containerization approaches
Preferred Qualifications
Advanced degree (PhD preferred) in Data Science or related field
Industry experience, or proven contributions to open source or competitions (such as Kaggle)
Experience with large-scale classification and recommender systems
Experience with large-scale Continuous Integration (CI) testing and analysis
Knowledge of MDLC (Model Development Life Cycle), to include Data Modeling, ETL, and Data Engineering
Practical experience in writing code or leading teams that leverage python data science libraries such as numpy, scikit-learn, pytorch, tensorflow etc.
Strong SQL optimization and performance tuning experience in a high-volume data environment that uses parallel processing. 
Familiarity with business intelligence and data visualization tools (such as Tableau).
Knowledge of Static Analysis, Compiler Design, and Programming Languages a plus.
GCP platform experience is a plus.
Understanding of data governance practices such as metadata management, data lineage, and data glossaries is a plus
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