Software Engineer (Applied Data Science) - Revenue Science, Ads Marketplace
Who We Are:
The ads marketplace team is responsible for placing each and every ad that Twitter serves. While placing those ads we decide how best to balance user experience, advertiser results, and Twitter revenue. Our team implements and builds software frameworks for the revenue marketplace, optimizes the ad delivery engine and manages demand/supply by employing software engineering and applied data science skills. We routinely deliver significant improvements to our revenue, and work in a close sync with our executive staff (including our COO & CFO) due to our direct impact on Twitter’s business.
What You’ll Do:
We’re looking for a key individual contributor to drive the marketplace framework forward. Ads marketplace decides which ads to serve billions of times a day under a sub-second setting. You will ship new features and optimize existing products. You will build high-quality software at scale, experiment, make data-driven decisions, optimize for impact, measure our product funnels, and apply machine learning and data science.
Your team will empower you with the autonomy to make good product decisions and ship well-engineered code. You’ll own significant projects end-to-end. The small teams of talented, passionate people in which you’ll work will include engineers and data scientists from across the revenue engineering organization. The ads marketplace team works on every high-priority ads project at Twitter.
Who You Are:
You’re a software engineer with a track record of delivering results. You find satisfaction in shipping code that delivers measurable impact in the form of immediate revenue. You want to work on a team that applies data science, either because that’s an existing strength of yours or you’d like it to become one. You’re looking to join a strong, high-performing team.
We collaborate with our serving and prediction teams to improve the early filters applied to eligible campaigns prior to predicting ad engagement rates
We evaluate the impact of ads on new Twitter users, determining how to show them ads in order to maximize their long-term usage of the product
We build more intelligent delivery algorithms resulting in better ROI for our advertisers
We own the ads engineering experimentation framework and are responsible for measuring the results of all experiments on Twitter ads
We build new ways for advertisers to buy ads on Twitter, such as paying up front for guaranteed results
- BS, MS, or PhD in computer science, data mining, machine learning, information retrieval, recommendation systems, natural language processing, statistics, math, engineering, operations research, or other quantitative discipline; or equivalent work experience.
- Experience in backend development in modern distributed system architectures at scale
Ability to thrive in an unstructured environment, working autonomously on a strong team to find opportunity and deliver business impact
Good understanding of (one or more of the following): Java, Scala, or C++
Good understanding of (one or more of the following): Python or R
Past experience in adtech
Experience in production stacks involving machine learning, AB testing, or control systems
Active contributor to a well-known open source project
Interesting side projects or Kaggle competition results
We are committed to an inclusive and diverse Twitter. Twitter is an equal opportunity employer. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any legally protected status.
San Francisco applicants: Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Engineering Hiring Process
Once your application is received, a recruiter will reach out pending your qualifications are a match for the role.
If your background is a match, you may have 1-2 technical phone interviews or be given the chance to provide a work sample depending on the role.
If the phone interviews go well or your work sample is strong, the final step includes interviews with 5-6 people held onsite in our office.
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