Data Scientist (NYT Cooking Recommendations)

Job Description

The New York Times is a technology company committed to producing the world's most reliable and highest quality journalism. Our ability to do so relies on an accomplished team of expert technologists who help NYT leverage the tremendous abundance of data unique to this company. We are looking for a Data Scientist to build recommendation algorithms for the NYT Cooking product.


You will join our diverse group of Data Scientists from academic and industry backgrounds. You will report to a Senior Data Scientist on the Algorithmic Recommendations team and work with the NYT Cooking group. In this role, you will:
  • Identify opportunities for serving personalized and non-personalized recommendations to our readers to help them discover new recipes and deepen their relationship with NYT Cooking
  • Develop features that capture our users' needs and preferences (including dietary preferences, levels of difficulty, and seasonal patterns)
  • Build algorithms that power recommendations in the NYT Cooking app, website, and newsletters
  • Work with a team of engineers, data scientists, and analysts to deploy and monitor new algorithmic capabilities
  • Design experiments that test the impact of new features and algorithms on user behavior and communicate findings to different audiences across the company

Technical Qualifications
  • PhD, MS, or 3+ years research or work experience in data science or a quantitative/computational discipline (including, but not limited to: computational social science, applied mathematics, cognitive and neural sciences, economics, computer science, physics, and statistics)
  • Experience designing performance metrics and using machine learning for feature engineering
  • Experience communicating technical concepts to expert and lay audiences
  • 2+ years coding experience, including Python
  • Experience in data engineering, including SQL and manipulating large structured or unstructured datasets for analysis
  • Experience with recommendation systems is a plus
  • Experience with NLP methods is a plus
  • Experience with experimental design is a plus
  • Experience working with Machine Learning models in a production environment is a plus

Non-Technical Qualifications
  • Desire to join the world's most important journalism company at a moment in history when the importance of learning from our data is transforming every aspect of the craft and practice of journalism
  • Excitement about using Machine Learning to improve the industry-leading digital cooking product (NYT Cooking has 750,000 paying subscribers!)
  • A passion for empirical research and for answering hard questions with data
  • Track record of solving challenging problems in academia and/or industry
  • Eagerness to work with colleagues in editorial, product management, marketing, and executive leadership groups


The New York Times is committed to a diverse and inclusive workforce, one that reflects the varied global community we serve. Our journalism and the products we build in the service of that journalism greatly benefit from a range of perspectives, which can only come from diversity of all types, across our ranks, at all levels of the organization. Achieving true diversity and inclusion is the right thing to do. It is also the smart thing for our business. So we strongly encourage women, veterans, people with disabilities, people of color and gender nonconforming candidates to apply.

The New York Times Company is an Equal Opportunity Employer and does not discriminate on the basis of an individual's sex, age, race, color, creed, national origin, alienage, religion, marital status, pregnancy, sexual orientation or affectional preference, gender identity and expression, disability, genetic trait or predisposition, carrier status, citizenship, veteran or military status and other personal characteristics protected by law. All applications will receive consideration for employment without regard to legally protected characteristics. The New York Times Company will consider qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local "Fair Chance" laws.