Machine Learning Scientist, Algorithmic Recommendations (Email Targeting)

The mission ( of The New York Times is to seek the truth and help people understand the world. That means independent journalism is at the heart of all we do as a company. It’s why we have a world-renowned newsroom that sends journalists to report on the ground from nearly 160 countries. It’s why we focus deeply on how our readers will experience our journalism, from print to audio to a world-class digital and app destination. And it’s why our business strategy centers on making journalism so good that it’s worth paying for.

About the Role, Mission or Department Overview

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.

Responsibilities:

• You will reframe business and newsroom goals as machine learning tasks that deliver accurate predictions, relevant insights, and optimization

• You will implement and deploy machine learning research with robustness and reproducibility, with consideration of risks and trade-offs

• You will learn new technologies and ML methods, and adapt them into our workflows

• You will deploy models behind production APIs or batch processes, collaborate with engineering teams, and integrate into processes throughout The Times

• You will 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.

Basic Qualifications:

• 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

Preferred Qualifications:

• 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

REQ-020137

The annual base pay range for this role is between:

$121,000 — $131,000 USD

Additional Compensation and Benefits

For roles in the U.S., dependent on your role, you may be eligible for variable pay, such as an annual bonus and restricted stock. Benefits may include medical, dental and vision benefits, Flexible Spending Accounts (F.S.A.s), a company-matching 401(k) plan, paid vacation, paid sick days, paid parental leave, tuition reimbursement and professional development programs.

For roles outside of the U.S., information on benefits will be provided during the interview process.

Candidate Use of GenAI Tools

We’re excited to learn more about you and your experience. To keep our hiring process as fair and authentic as possible, we ask that you submit your own work and not use GenAI tools to generate substantive content during the application and interview process.

If you’re an Engineering candidate, we’ll let you know what specific GenAI tools you are permitted to use for your technical assessment.

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