Be Part of What's Next
Hearst Magazines is seeking a visionary and hands-on Senior Data Scientist to join our dynamic Data & AI team. In this pivotal role, you will be a key player in architecting and implementing the advanced data models that power personalized user experiences, drive our next-generation advertising products, and provide deep, actionable insights to our world-class editorial and commercial teams. You will tackle complex challenges at the intersection of machine learning, user behavior, and content strategy, with a direct impact on the trajectory of our iconic brands.
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.
Benefits (What We Offer)
- Work with the Best: Collaborate with top-tier professionals across media, advertising, tech, fashion, lifestyle, and publishing, shaping the future of these dynamic industries.
- Grow Your Skills: Unlock your potential with access to innovative training programs, immersive workshops, and exclusive industry events.
- Work-Life Harmony: Enjoy the flexibility of hybrid work, empowering you to balance professional success with personal priorities.
- Foster Connection & Belonging: Join our Employee Resource Groups and help create a welcoming workplace where everyone feels valued and empowered.
Wellness First: Prioritize your well-being with a comprehensive benefits package that includes medical, dental, and vision insurance from Day 1.
The base salary for this role is between $149,000-$181,000. The actual base pay offered is dependent upon many factors, such as: transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future.
Hearst Magazines is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.
Skills Required
- 7+ years in a data science role with a proven track record of developing and deploying machine learning models in production environments.
- Expert proficiency in Python.
- Expert proficiency in SQL.
- Deep understanding of machine learning algorithms (regression, classification, clustering, NLP) and statistical modeling.
- Hands-on experience with machine learning libraries/frameworks such as Scikit-learn, TensorFlow, or PyTorch.
- Experience with big data technologies (Spark, BigQuery) and cloud platforms (GCP, AWS).
- Demonstrated leadership and mentorship experience, including leading cross-functional projects.
- Ability to translate complex business problems into data science projects and communicate technical concepts to non-technical stakeholders.
- Hybrid position based in New York City with an in-office requirement of four days per week.
- Master's or Ph.D. in a quantitative field (preferred).
Hearst Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Hearst and has not been reviewed or approved by Hearst.
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Healthcare Strength — Healthcare coverage is described as comprehensive, including medical plan choice, full in-network preventive coverage, dental and vision, telemedicine, prescription coverage, and fertility resources. Mental-health resources and other wellbeing services (e.g., therapy sessions, crisis support, virtual physical therapy, and chronic-condition programs) further strengthen the health offering.
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Retirement Support — Retirement support is positioned as meaningful through a 401(k) plan with company matching and Hearst covering plan administration fees. Performance bonuses are also noted as available in some roles, adding an additional rewards component beyond base pay.
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Leave & Time Off Breadth — Time-off benefits include paid holidays, paid sick days, and vacation time with a commonly cited annual range, alongside paid parental leave and family medical leave. A remote work program and flexibility signals are also included as part of the overall rewards experience.
Hearst Insights
What We Do
Hearst is a leading global, diversified media, information and services company with more than 360 businesses. Its major interests include ownership in cable television networks such as A&E, HISTORY, Lifetime and ESPN; global financial services leader Fitch Group; Hearst Health, a group of medical information and services businesses; transportation assets including CAMP Systems International, a major provider of software-as-a-service solutions for managing maintenance of jets and helicopters; 33 television stations such as WCVB-TV in Boston and KCRA-TV in Sacramento, California, which reach a combined 19 percent of U.S. viewers; newspapers such as the Houston Chronicle, San Francisco Chronicle and Times Union (Albany, New York); more than 300 magazines around the world, including Cosmopolitan, ELLE, Men's Health and Car and Driver, and digital services businesses such as iCrossing and KUBRA; and investments in emerging digital entertainment companies such as Complex Networks.
