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Full Time Job

Sr Machine Learning Scientist

Penguin Random House

New York, NY 10-31-2024
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  • Paid
  • Full Time
  • Senior (5-10 years) Experience
Job Description
Sr Machine Learning Scientist - (Open to remote)

The Data Science team at Penguin Random House is seeking an experienced Senior Machine Learning Scientist to drive the development of personalization products, which includes recommender systems for our websites, email programs, and online marketing.

As the world's leading publishing house, Penguin Random House remains at the forefront of digital transformation, using cutting-edge AI and machine learning techniques to shape the future of book discovery, sales, and customer engagement. With a commitment to quality and innovation, we leverage data science to enhance our capabilities across pricing, forecasting, personalization, and more.

Key Responsibilities:
• Lead and own the design, development, and deployment of end-to-end machine learning projects for large-scale recommender systems and personalization products.
• Develop models that power real-time online marketing tools, including customer segmentation, ad targeting, and user engagement prediction.
• Design and run A/B tests to validate model performance, iterating based on experiment results and user feedback.
• Collaborate with cross-functional teams including engineering, marketing, and product to integrate ML solutions into business products.
• Stay up to date with industry trends and advancements in recommender systems, personalization, and AI-driven marketing technologies.

Qualifications:
• 5+ years of professional experience in machine learning, with a strong focus on recommender systems, personalization, and online marketing audience targeting models.
• Expertise in Python and key ML libraries (e.g., TensorFlow/PyTorch, NVTabular, Triton).
• Experience with cloud-based services (e.g., AWS, Kubernetes, Databricks), containerization (Docker), and deploying ML models at scale.
• Strong knowledge of SQL for querying and managing large datasets.
• Ability to communicate technical concepts and results effectively to non-technical stakeholders.

Preferred Qualifications:
• Master's or PhD in a quantitative discipline like Statistics, Mathematics, Computer Science, Operations Research, or relevant work experience.
• Proven track record of building, deploying, and optimizing large-scale machine learning models in production environments.
• Experience in A/B testing, experimentation platforms, and online learning.
• Familiarity with MLOps tools and practices for managing the lifecycle of machine learning models in production.

Salary range for this role is $150,000 - $200,000. All positions are currently eligible for annual profit award or bonus, subject to company results.

Penguin Random House job postings include a good faith compensation range for each open position. The salary range listed is specific to each particular open position and takes into account various factors including the specifics of the individual role, and candidate's relevant experience and qualifications.

Full-time employees are eligible for our comprehensive benefits program. Our range of benefits include, but are not limited to, Medical/Prescription drug insurance, Dental, Vision, Health Care/Dependent Care Flexible Spending Account, Health Savings Account, Pre-Tax and Roth 401(k), Short and Long-Term Disability Insurance, Life/AD&D Insurance, Commuter Benefits, Student Loan Repayment Program, Educational Assistance & generous paid time off.

Jobcode: Reference SBJ-gm7192-3-145-7-187-42 in your application.

Salary Details
Salary Range: $125,000 to $200,000 Per Year ($ USD)
Company Profile
Penguin Random House

Penguin Random House is the leading adult and children’s publishing house in North America, the United Kingdom and many other regions around the world. In publishing the best books in every genre and subject for all ages, we are committed to quality, excellence in execution, and innovation throughout the entire publishing process: editorial, design, marketing, publicity, sales, production, and distribution.