Job Description

About Hearst Magazines ( Why Us?)

Hearst Magazines’ portfolio of more than 30 iconic brands in the U.S.—including Cosmopolitan, ELLE, Esquire, Good Housekeeping, Harper’s BAZAAR, and Popular Mechanics — inspires, entertains, and builds new and bold experiences for an engaged and growing audience across digital, video, social and print, reaching nearly 130 million readers and site visitors each month. With sophisticated content creation, cutting-edge technology, and industry-leading data capabilities, we make media and products that move people across all platforms. We are a global media company that publishes nearly 200 magazine editions and 175 websites around the world—and together, we are shaping what’s next. 

Key Responsibilities(What are you doing)

• Lead and Innovate: Design, develop, and deploy sophisticated machine learning models to address core business challenges, including user segmentation, content recommendation, churn prediction, and lifetime value modeling.
• Strategic Impact: Partner with product, engineering, editorial, and revenue teams to identify and execute on data science initiatives that drive measurable business outcomes.
• Full-Cycle Development: Own the end-to-end data science workflow, from hypothesis generation and data exploration to model building, validation, and deployment into production environments.
• Advanced Analytics: Conduct in-depth exploratory analysis of large, complex datasets to uncover hidden patterns, trends, and opportunities for innovation.
• Mentorship: Provide guidance and mentorship to junior data scientists and analysts, fostering a culture of technical excellence and collaborative problem-solving.
• Thought Leadership: Stay at the forefront of advancements in data science and machine learning, and champion the adoption of new technologies and methodologies within the organization.

Qualifications (What We’re Looking For)

• Experience: 7+ years of experience in a data science role, with a proven track record ofdeveloping and deploying impactful machine learning models in a production environment.
• Technical Expertise: 
  • Expert proficiency in Python and SQL.
  • Deep understanding of machine learning algorithms (e.g., regression, classification, clustering, NLP) and statistical modeling.
  • Hands-on experience with machine learning libraries and frameworks such as Scikit-learn, TensorFlow, or PyTorch.
  • Experience with big data technologies (e.g., Spark, BigQuery) and cloud platforms (GCP, AWS).
• Strategic Mindset: Ability to translate complex business problems into well-defined data science projects and communicate technical concepts effectively to non-technical stakeholders. 
• Leadership: Demonstrated ability to lead projects, mentor junior team members, and collaborate effectively across cross-functional teams.
• Education: Master's or Ph.D. in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline is preferred.
• This is a hybrid position based in New York City, with an in-office requirement of four days per week.