Machine Learning Scientist, Algorithmic Recommendations (Email Targeting)

The New York Times•New York, NY

23d•$121,000 - $131,000

About The Position

The New York Times is committed to producing the world's most reliable and highest quality journalism. Our ability to do so relies on a talented team of expert technologists who help NYT learn from a tremendous abundance of data unique to this company. The Algorithmic Recommendations and Audience Data Science team aims to help users discover relevant content across the Times' website, apps, and emails. To achieve this, the team applies algorithms that make use of information about our readers' behavior and our editorial judgment. We also build internal tools for the newsroom to better understand story performance and coverage trends. We are looking for a Machine Learning Scientist to join the team and apply machine learning methods to our targeted email strategy. You will report to the lead of the AlgoRecs team.

Requirements

PhD, MS + 2 years experience, or 3+ years experience in statistics, computational social science, applied mathematics, economics, or another quantitative/computational discipline
2+ years experience with open source machine learning or statistical analysis tools
2+ years coding experience in Python
2+ years experience in SQL and manipulating large structured or unstructured datasets for analysis
Experience in A/B testing or experimentation

Nice To Haves

1+ years of experience applying machine learning to email campaigns (optimizing timing, content selection, or audience creation)
1+ years of experience with recommendation systems, using natural language processing and large language models
1+ years of experience translating ambiguous business questions into machine learning problems
1+ years of experience building data products, either internal or consumer-facing

Responsibilities

Reframe business and newsroom goals as machine learning tasks that deliver accurate predictions, relevant insights, and optimization
Implement and deploy machine learning research with robustness and reproducibility, with consideration of risks and trade-offs
Learn new technologies and ML methods, and adapt them into our workflows
Deploy models behind production APIs or batch processes, collaborate with engineering teams, and integrate into processes throughout The Times
Communicate complex ideas in machine learning while collaborating with all kinds of colleagues in in engineering, analytics, product management, marketing, editorial, and executive leadership groups
Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world.