Staff Applied Scientist I

Company Description

Etsy is the global marketplace for unique and creative goods. We build, power, and evolve the tools and technologies that connect millions of entrepreneurs with millions of buyers around the world. As an Etsy Inc. employee whether a team member of Etsy, Reverb, or Depop, you will tackle unique, meaningful, and large-scale problems alongside passionate coworkers, all the while making a rewarding impact and Keeping Commerce Human

Job Description

What’s the role?

Etsy is an international marketplace with 100M+ handmade and vintage items from passionate artists, designers and vintage collectors. Our Recommendations and Personalization (PersoEng) enables product teams across Etsy to understand, personalize, and rank recommendations that we present to buyers.

As a Staff Applied Scientist, you will be responsible for initiating and delivering new solutions for the domain application of Recommendations. We are looking for individuals who are product & technology driven, and are passionate about making ML innovations in areas such as: Natural Language Processing, Representation Learning, Clustering, Optimization, Deep Learning, Graph Learning, and Reinforcement Learning to help improve the Etsy buyer/seller experience!

This is a full-time position reporting to the Applied Science Manager and the base salary range will be $196,000 - $230,000 USD per year. In addition to salary, you will also be eligible for an equity package, an annual performance bonus, and our competitive benefits that support you and your family as part of your total rewards package at Etsy. This role requires your presence in Etsy’s Brooklyn office in an in-person or flex capacity. Candidates living within commutable distance of the Brooklyn Hub, may be the first to be considered. Etsy offers different work modes to meet the variety of needs and preferences of our team. Learn more about our Flex and Office-based work modes and workplace safety policies here.

What’s this team like at Etsy?

The Retrieval team builds capabilities to select relevant item-candidates to help product teams to provide personalized user experiences. We use machine learning algorithms to train models which can be used to choose relevant personalized candidate-sets for our recommendation engine. We are looking for an experienced machine learning scientist who can help build Etsy’s next generation capabilities for the candidate generation.

What does the day-to-day look like?

•  Initiate and lead a new track of works to enhance the capability of candidate generation for Etsy’ Recommendation engine.
• Develop innovative representations to capture salient signals of our users and listings, including aspects like content, function, budget, and preferences.
• Develop models with custom architecture or objective functions that target Etsy-specific problems, including but not limited to personalized retrieval, ranking, revenue optimization, seller fairness, and seasonality
• Mentor other applied scientists and guide them during their projects
• Work closely and collaboratively with software engineers, data scientists, product managers, and designers
• Of course, this is just a sample of the kinds of work this role will require! You should assume that your role will encompass other tasks, too, and that your job duties and responsibilities may change from time to time at Etsy's discretion, or otherwise applicable with local law.

Qualifications

Qualities that will help you thrive in this role are:

• Successful track record of applying deep learning machine learning techniques in addressing real-world problems and looking for opportunities to make impacts
• Have extensive experience with machine learning and deep learning solutions across the entire machine learning stack, from data collection to evaluation.
• Ability to take on and lead unconstrained problems and know when to seek help and how to deliver
• Ability to write and manipulate data at a large scale, in distributed environment
• Shown success in developing productionized machine learning solutions. You are comfortable with using git, docker, airflow and other tools for developing robust, production-ready code
• Have strong verbal and written communication skills.

Preferred requirements:

• An M.S. or Ph.D. degree in Computer Science or related fields, with 5+ years of practical machine learning experience