Senior Machine Learning Engineer

For more than 160 years, we've used the strength of our brands to lead with our values and make an outsized impact on the world. We are taking one of the world's most iconic brands into the next century: from creating machine learning-powered denim finishes to using block-chain for our factory workers' wellbeing, to building algorithms to better meet the needs of our consumers and optimize our supply chain. As the Machine Learning Engineer, you will work along side the Data Science team to operationalize the Machine Learning Models in Production on a broad set of domains that power a data-driven transformation of our standard business procedures across channels and organizations. \- Work with data scientists and analysts to create and deploy new product features on the ecommerce website, in-store portals and the Levi's mobile app \- Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation \- Write efficient software to ship products in an iterative, continual- release environment \- Contribute to and promote good software engineering practices across the team \- Contribute to and re-use community best practices \- Embody the values and passions that characterize Levi Strauss & Co., with empathy to engage with colleagues from multiple backgroundsExample Projects Besides driving the transformation of Levi's into a data-driven enterprise in general, here are some specific projects you will work on and contribute to: \- Personalized in-session product recommendation engine \- Customer Segmentation \- Automated text summarization and clustering \- Next-Best offer prediction \- Design Microassortments for Next-Gen stores \- Anomaly detection and Root Cause Analysis \- Unified consumer profile with probabilistic record linkage \- Visual search for similar and complementary productsMore \- University or advanced degree in engineering, computer science, mathematics, or a related field \- 7+ years experience developing and deploying machine learning systems into production \- Experience working with a variety of relational SQL and NoSQL databases \- Experience working with big data tools: Hadoop, Spark, Kafka, etc.