Machine Learning Scientist at The New York Times | JobProMax

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. Its why we have a world-renowned newsroom that sends journalists to report on the ground from nearly 160 countries. Its 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 its why our business strategy centers on making journalism so good that its worth paying for.

About the Role

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 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 content across the Times' website, apps and emails by applying 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 a group of machine learning scientists and data analysts that partner with teams across The New York Times. We are looking for a Machine Learning Scientist to join the team and apply machine learning methods to meet this challenge. You will report to a Lead Machine Learning Scientist, and collaborate with partners across the company.

Responsibilities:

You will reframe business and newsroom goals as machine learning tasks that deliver accurate predictions, relevant insights, and optimization
You will adapt or develop machine learning algorithms in cases when existing algorithms are insufficient, while implementing simple approaches when appropriate
You will implement and deploy machine learning research with robustness and reproducibility, with consideration of risks and trade-offs
You will turn models into data products, collaborate with engineering teams, and integrate into processes throughout The Times
You will communicate complex ideas in machine learning while collaborating with technical and non-technical 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 work experience in machine learning, statistics, computational social science, applied mathematics, 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

Preferred Qualifications:

1+ year of experience with recommendation systems, including content-based methods using natural language processing
1+ years of experience with deep learning architectures, including embedding methods and Pytorch or Tensorflow
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-019073

The annual base pay range for this role is between:

$121,000—$131,000 USD

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.

The New York Times Company is committed to being the worlds best source of independent, reliable and quality journalism. To do so, we embrace a diverse workforce that has a broad range of backgrounds and experiences across our ranks, at all levels of the organization. We encourage people from all backgrounds to apply.

We are an Equal Opportunity Employer and do 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 U.S. Equal Employment Opportunity Commission (EEOC)s Know Your Rights Poster is available here.

The New York Times Company will provide reasonable accommodations as required by applicable federal, state, and/or local laws. Individuals seeking an accommodation for the application or interview process should email reasonable.accommodations@nytimes.com. Emails sent for unrelated issues, such as following up on an application, will not receive a response.

The Company encourages those with criminal histories to apply, and will consider their applications in a manner consistent with applicable "Fair Chance" laws, including but not limited to the NYC Fair Chance Act, the Los Angeles Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.

For information about The New York Times' privacy practices for job applicants click here.

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If you see a fake or fraudulent job posting, or if you suspect you have received a fraudulent offer, you can report it to The New York Times at NYTapplicants@nytimes.com. You can also file a report with the Federal Trade Commission or your state attorney general.

Machine Learning Scientist

The New York Times

Job Description

Responsibilities:

Basic Qualifications:

Preferred Qualifications:

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