Job summary
Job post source
This job is directly from Shopbop, part of Amazon.
Job overview
The Applied Scientist at Shopbop focuses on developing machine learning models and science-enabled pipelines to solve business problems and advance Shopbop's mission as a fashion retailer.
Responsibilities and impact
The role involves identifying appropriate models for ambiguous problems, building development plans for new science features, overseeing peer software engineers, developing critical code, and aligning leadership on approaches.
Compensation and benefits
The base salary ranges from $150,400 to $260,000 depending on geographic location and experience, with potential equity, sign-on bonuses, and comprehensive medical and financial benefits.
Experience and skills
Candidates need 3+ years of machine learning model building, a PhD or Master's with 6+ years applied research, programming skills in Java, C++, or Python, and experience with neural deep learning; preferred skills include R, scikit-learn, Spark MLLib, Tensorflow, and large-scale distributed systems.
Career development
The role offers exposure to senior leadership, mentoring opportunities, and influence over science tools and methodologies in a growing science adoption environment.
Work environment and culture
Shopbop values inclusivity and collaboration, with a culture empowering employees to deliver results and a focus on accommodating diverse needs.
Company information
Shopbop is a leading fashion retailer and part of Amazon, known for innovation in fashion e-commerce and technology integration.
Team overview
The candidate will join the 35-person Shopbop Catalog team supporting various business units across multiple US locations including Madison, New York, and Las Vegas.
Job location and travel
The job is based in multiple US locations including Madison WI, New York NY, and Las Vegas NV, with no specific travel requirements mentioned.
Application process
Applicants should apply via Shopbop's internal or external career site; accommodations are available for disabilities during hiring.
Unique job features
The position offers the chance to work on cutting-edge ML-enabled features, influence Shopbop's science strategy, and collaborate with Amazon and Zappos technologists.
Company overview
Shopbop is a leading online fashion retailer specializing in contemporary and designer apparel, shoes, bags, and accessories for women. Founded in 1999 and acquired by Amazon in 2006, Shopbop has built a reputation for curating a diverse selection of high-end and emerging brands. The company generates revenue through direct-to-consumer sales on its e-commerce platform, leveraging its strong brand partnerships and exclusive collaborations. Shopbop's history of innovation in online retail and its integration with Amazon's logistics and customer service infrastructure are key elements that candidates should be aware of.
How to land this job
Position your resume to highlight your expertise in building machine learning models for business applications, emphasizing your experience with neural deep learning methods and programming languages like Python, Java, or C++.
Focus on showcasing your ability to collaborate cross-functionally with business leaders, software engineers, and data engineers, as well as your experience in designing and launching ML-enabled features and science pipelines that solve complex catalog and inventory problems.
Apply through multiple channels including Shopbop’s corporate career site and LinkedIn to maximize your chances of being noticed for the Applied Scientist role.
Connect with current Shopbop Catalog Engineering team members on LinkedIn, initiating conversations by referencing recent Shopbop or Amazon innovations in ML or asking about the team’s approach to integrating science into production pipelines as effective ice breakers.
Optimize your resume for ATS by incorporating keywords from the job description such as 'machine learning models,' 'neural deep learning,' 'Python,' 'distributed systems,' and 'catalog engineering' to ensure your resume passes automated screenings.
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