At Try Before You Buy (TBYB) , our vision is to create a compelling global styling business that becomes customers’ most trusted fashion advisor. In addition to providing the convenience of recurring recommendations through Personal Shopper boxes, we will use customers’ personal preference information to enhance all shopping experiences.
Try Before You Buy team at Amazon Fashion is looking for an Applied Scientist to join us to build our next-generation personalized recommendation systems for Personal Shopper and Prime Wardrobe. In this role, you will be responsible for researching, developing, and deploying machine learning, computer vision, and NLP models to make customers' fashion shopping experience at Amazon engaging and joyful.
The primary responsibilities of this role include:
· Lead complex projects that design and build machine-learning, natural language processing, and computer vision solutions for our customers
· Collaborate with PMs in Designers on customer-facing experiences that will utilize this data to transform the apparel shopping experience for Amazon customers.
· Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management.
· Drive continued scientific innovation as a thought leader and practitioner.
· Provide technical and career development guidance to both scientists and engineers in the organization.
· Build ETL pipelines to collect and process data
· Frame and transform ambiguous business challenges into science hypotheses. Design and implement offline and online experiments to evaluate them
· Develop prototypes to test new concepts/proposals for models and algorithms
· Design and build automated, scalable pipelines to train and deploy ML models
· 5+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
· 4+ years working as a Data Scientist
· Experience with Spark, Hadoop, Elastic Map Reduce (EMR)
· Leadership experience in a research team, including planning, mentoring, and ownership of group outcomes.
· Communication and data presentation skills
· A natural curiosity and desire to learn
· Ability to distill problem definitions, models, and constraints from informal business requirements, and to deal with ambiguity and competing objectives
· Published research work in academic conferences or industry circles
· Experience in designing and building scalable machine learning models
· Experience in major machine learning software frameworks such as scikit-learn, MXNet, TensorFlow, PyTorch, Keras, etc.
· Experience with Big Data technologies such as Hadoop, Spark, Pig, Hive, Presto, HBase, etc.