Applied Researcher

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.

Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

About the role and the team:

Advertising is one of the fastest growing areas in eBay which in some ways, is defining the future of eBay. Digital advertising as an industry is growing rapidly, and the landscape is shifting as ecommerce advertisers are finding better value with ecommerce companies like eBay. As they shift budget from the duopoly of Google and Facebook, it builds a huge opportunity for eBay. Advertising is also amplifying eBay’s ecommerce by providing a tool for our sellers to move inventory and for buyers, by surfacing high quality items.

This team focuses on building ML/data services for our advertiser sellers, to guide them ways to optimize for their ad budget and goals, for example by recommending the right inventory, keywords, budget and Ads bid rate to apply for their campaigns, diagnose issue while providing solutions, and eventually create/optimize campaigns automatically for advertisers for the best performance. This is a relatively new area but with a very high business potential and need. It would allow you to work with extensive amounts of data, and use a variety of data science techniques.

As an Applied Researcher within our Advertising team, you will play a pivotal role in developing machine learning models and algorithms to guide advertisers optimally. This role will also include a scope positioned around data analysis. Your work will directly impact our guidance systems, enhancing ad performance and delivering actionable insights. You will lead our efforts in data analysis, uncovering trends, and driving data-informed decisions that support our advertisers' success.

What you will accomplish:

• Machine Learning Development: Design, implement, validate and deploy machine learning models tailored to advertising applications. Focus on developing systems that provide actionable guidance to advertisers, optimizing ad performance and engagement.

• Data Understanding: Analyze large volume of production data to produce business insights and identify potential opportunities for ML solutions

• Cross-functional Collaboration: Work closely with product managers, engineers, and other researchers to integrate machine learning insights into our advertising products. Ensure that our advertiser guidance systems are aligned with user needs and business goals.

• Innovation and Research: Stay abreast of the latest developments in machine learning and advertising technologies. Chip in to internal and external research communities by publishing findings, attending conferences, and participating in collaborative projects.

• Technical Mentoring: Guide junior researchers and data analysts. Share knowledge and standard methodologies in machine learning and data analysis to uplift the team's capabilities.

What you will bring:

• Education: Ph.D. or M.S. in Computer Science, Statistics, Mathematics, or a related field with a focus on machine learning or data science.

• Experience: 5+ years of experience in applied research or development of machine learning models, data analysis, preferably with the advertising industry or a closely related field, i.e. search, recommender system and NLP.

• Technical Proficiency: Strong programming skills in Python, Scala, or similar languages. Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch) and experience with big data technologies (e.g., Hadoop, Spark).

• Analytical Skills: Outstanding ability to analyze large datasets, identify trends, and derive actionable insights. Expertise in data mining and segmentation. SQL is a must.

• Communication: Excellent verbal and written communication skills, with the ability to convey sophisticated technical concepts to non-technical partners.

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