Data Engineer, Algorithmic 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

We are looking for a Software Engineer to join our Algorithmic Targeting (ALTA) team to solve creative challenges by building infrastructure and integrations to deploy and test machine learning models that help grow our subscriber base and lifetime value. 

At The New York Times, our machine learning services support our mission to help people understand the world and our business goal of acquiring and retaining subscribers. We accomplish this by algorithmically targeting our readers at specific moments in their subscription lifecycle, driving lifetime value (LTV) and subscriber base growth. By joining our team, you’ll help the numerous voices in The New York Times News, Games, Cooking, Wirecutter, and The Athletic newsrooms reach everyone through our growing subscription business.

This is a hybrid role based in our New York City headquarters, reporting to the Senior Engineering Manager, Access and Messaging Systems. You can typically expect to come into the office 2 days per week.

Responsibilities:

• Build infrastructure for machine learning modeling, partnering with data scientists
• Collaborate with data scientists to deploy realtime and batch machine learning models, driving millions of onsite interactions
• Contribute to the Times’ Machine Learning Platform to help build efficient real-time algorithms
• Build ETL data pipelines, partnering with data analysts
• 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:

• 2+ years of experience writing production-quality Python
• 1+ years of experience building and orchestrating ETL pipelines (e.g. Apache Airflow, DBT Cloud, BigQuery)
• 1+ years of experience with SQL
• 1+ years of experience with building, transforming and modeling reusable datasets for Machine Learning and analytics use cases
• Experience with cloud computing platforms (e.g. GCP, AWS)
• Familiarity with Git, and CI/CD systems such as Drone or Jenkins
• Experience communicating to non-technical stakeholders

Preferred Qualifications:

• 1+ years of experience building distributed and scalable platforms for machine learning
• 1+ years of experience deploying containerized applications with Docker and Kubernetes
• Experience building unit and integration tests
• Experience programming in Go
• Experience with infrastructure as code (e.g. Terraform)
• Familiarity with ML packages/tools such as scikit-learn, TensorFlow, PyTorch, and/or MLflow

This role may require limited on-call hours. An on-call schedule will be determined when you join, taking into account team size and other variables

#LI-Hybrid

The annual base pay range for this role is between:$104,000—$130,000 USD

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 c