About Your Role:

Dotdash Meredith is the largest premium publisher in the world. Every day tens of millions of people trust our diverse brands to help them learn, make decisions, and find inspiration. We fund our digital content with digital advertising, but we believe the only way to maintain the loyalty of our audience is with faster, better, privacy-safe ads. And, as the world moves away from intrusive, cookie-based advertising, we are ready with D/Cipher, our intent-based advertising system. 

As a Senior Data Scientist, you will build models and systems that help our ad sales and operations teams to connect premium advertisers to our premium brands, maximize performance, and optimize yield. Advertising is a complex and rich problem space for a data scientist and your solutions will require the application and integration of a wide range of tools and techniques including: recommendation systems, forecasting, revenue optimization, natural language processing, and reinforcement learning as well as an understanding of how people use data products to drive decision-making.

About Your Contributions:

• Collaborate with stakeholders to understand business requirements and formulate data-driven solutions.

• Develop, train, and validate models on large datasets.

• Utilize advanced machine learning techniques to improve model accuracy and performance.

• Work closely with business partners to identify and prioritize modeling needs.

• Work closely with the engineering team to integrate models into production systems.

• Monitor model performance in production and make improvements as necessary.

• Stay up-to-date with the latest developments in machine learning and bring new techniques and technologies to the team.

About You:

• PhD or Master’s degree in Computer Science, Mathematics, Statistics or a related field, or equivalent work experience.

• 3+ years of experience in data science and machine learning with a strong focus in one of the following: advertising, recommendation systems, quantitative finance, or yield optimization.

• Excellent data science fundamentals, with a solid grasp of statistics and modeling techniques.

• Strong programming skills in Python and experience with standard data science tools and libraries (Pandas, NumPy, Scipy, NLTK, Spark, Docker, TensorFlow, PyTorch, scikit-learn, etc.).

• Experience deploying machine learning models in production on at least one cloud platform (GCP, AWS, etc.).

• Excellent communication skills and the ability to work effectively with cross-functional teams.

• Able to independently and fluently translate business needs to data problems.

• Excellent ability to communicate results and progress to non-technical and technical audiences.

• Proven history of leading projects from start to finish and shipping high-quality, scalable code.

• Ability to balance multiple projects and priorities from numerous stakeholders with quick turnarounds.

• Strong problem-solving skills and the ability to think creatively.