Staff Data Scientist

We're seeking talented Data Scientists who want to make a tangible impact on company metrics. The results of our models and experiments are seen almost instantly—our learning loops are measured in hours and minutes, not weeks or days. This is arguably one of the fastest model-learning environments in the world. We've built an infrastructure that enables models to be deployed at both scale and speed. With a growing variable set of hundreds of potential features, this is a fertile environment for building, experimenting, and achieving real impact. When a model works, the business value is immediate.

Who You Are

A successful candidate possesses deep expertise in Ad Tech, AI/ML, and Data Science, especially at scale. You should be comfortable with big data processing and cloud computing.

• Skills & Expertise: You have a strong mathematical aptitude in areas like Statistics, Probability Theory, and Machine Learning. You're also proficient with data science languages and tools like Python or Apache Spark, which are essential for designing scalable solutions, implementing proof-of-concept models, and evaluating them both offline and online. You'll work closely with engineers to bring these solutions into production and drive business value.
• A Curious Mind: Most importantly, you are passionate about investigating and learning from data, asking provocative questions, and are driven to build models that have a real business impact.
• Diverse Backgrounds Welcome: We value diverse academic backgrounds, so long as you demonstrate the intent to think and problem-solve like a data scientist. Our team includes engineers, mathematicians, computer scientists, physicists, economists, and social scientists—a great data scientist can come from any academic field.

Required Qualifications

• A Master’s degree in a quantitative field such as Computer Science, Electrical Engineering, Statistics, Mathematics, or Economics. A Bachelor's degree from a reputable college with additional experience is also acceptable.
• 7-10 years of work experience in a quantitative field, including experience in model building and validation.
• Experience in the Ad Tech industry is a plus, where you would have applied machine learning and statistical techniques to solve real-world problems, particularly on large datasets.
• Proficiency with software programming and statistical platforms like R or Python, including visualization tools.
• Comfortable with the big data ecosystem and Apache Spark. Familiarity with Microsoft Azure, AWS, or Google Cloud/Vertex AI is a bonus.
• Ability to collaborate effectively with cross-functional teams.
• Familiarity with the challenges of a world without identity data, especially for iOS and Android.
• Excellent technical and business communication skills; you can present complex technical ideas in a simple, understandable way.
• A high degree of curiosity and the ability to learn new areas rapidly.

What You'll Do

You will be responsible for leading one or more data science initiatives. This includes project ideation, solution design, measurement, iteration, coaching, deployment, and post-deployment management. You'll also design, develop, and test product experiments.

• Hands-On Work: This is a hands-on role where you will actively analyze data, design and develop models, and solve problems with the rest of the team.
• Stakeholder Management: You'll be the interface between the data science team and internal stakeholders, including Product, Engineering, Data, and Business teams.
• Thought Leadership: We encourage and support team members to publish blogs and case studies and to speak at industry conferences to share their work.
• Model Building & Learning: You'll learn to identify business problems where AI can have the greatest impact, anchoring your model design in business context and end-user needs. You will gain experience in connecting model impact with measurable business results.
• Collaborative Environment: You'll work in a multi-functional team, collaborating with a diverse group of individuals from engineering, product, and business teams.
• Experimentation: You’ll have the opportunity to experiment with multiple algorithms. Our environment enables you to quickly build, launch, and review the performance of different approaches.
• Creative Problem-Solving: Importantly, you’ll learn to be creative in your model design. Our models must be tailored to specific problems, often requiring layers of models and feedback mechanisms to succeed in a dynamic environment.