Applied Scientist II

Who we are

The Trade Desk is a global technology company with a mission to create a better, more open internet for everyone through principled, intelligent advertising. Handling over 1 trillion queries per day, our platform operates at an unprecedented scale. We have also built something even stronger and more valuable: an award‑winning culture based on trust, ownership, empathy, and collaboration. We value the unique experiences and perspectives that each person brings to The Trade Desk, and we are committed to fostering inclusive spaces where everyone can bring their authentic selves to work every day.

Do you have a passion for solving hard problems at scale? Are you eager to join a dynamic, globally connected team where your contributions will make a meaningful difference in building a better media ecosystem? Come and see why Fortune magazine consistently ranks The Trade Desk among the best‑sized workplaces globally.

The Trade Desk helps marketers unlock the potential of the open internet, on every channel and device, across the entire customer journey. Our Identity products power intelligent ad buying on TTD. Our Applied Scientists in the Identity team are end‑to‑end owners of TTD’s identity products. This end‑to‑end ownership gives the opportunity to participate in designing, researching, building, and delivering data‑focused and high‑impact products for our stakeholders.

These products are based on large‑scale data that are in the order of tens of millions of queries per second. Working with such large‑scale data requires our applied scientists to be at the cutting edge of both the data processing frameworks as well as scalable algorithms.

Our Applied Scientists also serve as an ambassador to our business partners to champion and evolve our identity products as the identity landscape of the open internet changes. They understand the business requirements and current design choices; they communicate effectively with business partners on opportunities and challenges; and they are strategic partners in creating the next generation of identity products.

Applied Scientists contribute to more than our product – they build up our team. We are a team built on a foundation of generosity and openness, and we expect our Applied Scientists to help make others better and raise the bar for those around them.

Some of the work that you will be doing to help us deliver on our mission is:

• Make significant, self‑directed contributions to components of large and impactful data‑focused projects. You will think beyond just the task at hand to deeply understand the 'why' behind what you are doing.
• Work with data from the open internet that is at large scale—think of tens of millions of transactions per second.
• Utilize your strong sense of data intuition. At our scale, many off‑the‑shelf modeling techniques (open source and enterprise) simply don’t work. You will work from first principles and intuition to develop solutions and adapt them to a unique environment.
• Leverage your broad familiarity with basic concepts in probability and statistics, along with exposure to basic foundations of computer science, graph mining, and machine learning.
• Unlock your product‑focused mindset. With your passion and potential, you’ll contribute to the process of discovering what will delight our stakeholders and drive forward one of the world’s largest and most influential industries toward a vision of openness, transparency, and evidence‑based decision‑making.
• Work with confidence and without ego. Our applied scientists have deep knowledge and exercise a high level of leadership in their daily work. You’ll have strongly‑held, defensible ideas, and advocate for what you believe is right. You’ll practice identifying and evaluating trade‑offs, willing to be proven wrong, and be committed to support your fellow teammates.
• Think creatively, not bound by "the way things have always been done."
• You will have the opportunity, based on your interests and background, to work on problems related to graph mining and graph merging; algorithmic optimization on petabytes of data; and theoretical information approaches for…