Sr Applied Researcher | NYC | Machine Learning Expert | Will Sponsor!
- New York, NY
Our Merchandising and Advertising team works on delivering recommendations at scale and in near real time to our buyers on our website and native apps. We work on hard data science, engineering and UX challenges across the entire recommendations workflow - from selecting and ranking the most relevant listings to recommendations from our top online vendors inventory of over 1 billion live listings based on the user’s implicit shopping intent and context, to delivering and rendering them on top leading web pages and native apps using appropriate user interfaces.
Recommendations are a core part of how our buyers navigate vast and varied inventory. At the same time. sponsored recommendations is growing rapidly as a channel for our sellers to drive sales velocity by promoting their listings. Apply and learn who we are and what we can offer you by joining our team!
More About the Role...
- We are looking for candidates who has a deep passion for developing state of the art Machine Learning based approaches for recommendations and monetization, and deploying them to production reliably and at scale. You will collaborate with product managers, applied researchers and engineers to invent, design and implement end-to-end reliable, scalable, efficient production services in Scala/Java, using state-of-the-art Big Data, Machine Learning, Deep Learning and Optimization techniques.
- Our ideal candidate will have a blend of applied ML research and engineering skills, proven track record of solving critical business problems through data science and strong analytical/quantitative and engineering skills. You will be expected to have strong communication skills and to be capable of cross-group collaborations.
- Track the state of art and help shape our applied research on recommender systems - both organic and sponsored
- Drive applied research in monetization of recommendations via first party ads by translating state of art into practical solutions
- Mentor data scientists and engineers in the application of ML, revenue optimization approaches
- Connect with senior data scientists in partner domains and drive key partnership initiatives
- Drive internal and external publications of applied research
- PhD in Machine Learning or related area
- 5-10 years professional experience post PhD
- Deep understanding of Machine Learning and Data Science
- Experience with applying ML to recommender systems at an enterprise scale
- Experience with sponsored recommendations and revenue optimization
- Applied research experience with a publication track record
- Familiarity with Big Data technologies such as Spark and Hadoop
- Good communication skills to convey ideas and solutions
- Ability to come up with good solutions quickly and prototype them rapidly
- Experience with guiding research teams and mentoring individual researchers
- Ability to prioritize business needs and pivot quickly if needed
Skills and Certifications [note: bold skills and certification are required]
- Machine learning
- Recommendations Systems
- Applied research
Security Clearance Required: No
Visa Candidate Considered: Yes
Base Salary - $175,000 to $220,000-DOE
Benefits - Full
Relocation Assistance Available - Yes
Commission Compensation - No
Bonus Eligible - No
Overtime Eligible - No
Interview Travel Reimbursed - No
10+ to 15 years experience
Seniority Level - Mid-Senior
Management Experience Required - No
Minimum Education - Master's Degree
Willingness to Travel - Never
Upon applying please note the following Pre-Screening Questions
1) Do you have work authorization to work legally in US?
2) Can you relocate to NY for this job?
Our Ideal Candidate will be as follows:- Have a PhD in Machine Learning or related area- Have 5-10 years professional experience post PhD- Have experience in E commerce industry - Have experience with computational advertising- Have some relevant publications-Have solid industry experience- Will Work Onsite | No remote work option
Thursday, April 1, 2021
$175,000 to $220,000