The Trade Desk is looking to solve measurement problems centered around Advertising Technology by applying state-of-the-art modeling techniques. This role will help address the growing demand for advanced optimization across multiple channels on their platform.
Requirements
- Proficient in open-source languages, you have a strong passion for enhancing and expanding your technical skills.
- Hands-on development of statistical and machine learning models and solutions utilizing open-source tools and cloud computing platforms.
- Has a deep understanding of the foundations of statistics and machine learning models.
- Hands-on experience building models at scale.
- Experience in uplift model and causal inference
- Experience of designing experiments in a production environment.
- Proficient in Python.
Responsibilities
- Explore, evaluate and deploy models to solve the problem of “whom to target on each channel”
- Explore data from various sources within TTD and prototype ETL pipelines to collect them for model training.
- Design and analyze experiments (e.g. A/B test) to validate model results.
- Implement research work in production.
- Work with cross-functional stakeholders to come up with the best implementation and testing plan.
- Communicate learnings from data and insights from models in compelling ways to influence product decisions.
- Mentor junior team members on the same team about technical topics.
Other
- A track record of owning a project end-to-end (from research to production), and partnership with a cross-functional team of data scientists, engineers and product managers to deliver advanced analytics or models.
- Possessing a keen sense of data intuition and the ability to innovate in the field of causal inference and/or machine learning, as evidenced by achievements like first-author publications or project successes.
- BS/MS with 6+ years or a PhD with 4+ years of experience working in a DS or ML role that involves bringing products from ideation to production.
- Experience running heavy workloads on a distributed computing cluster (especially EMR or Databricks), leveraging technologies like Spark to work with large datasets preferred.
- The ability to communicate with diverse stakeholders, making architecture recommendations, ensuring effective execution, and measuring quality of outcomes.
