Condé Nast is a global media company producing the highest quality content with a footprint of more than 1 billion consumers in 32 territories through print, digital, video and social platforms. The company’s portfolio includes many of the world’s most respected and influential media properties including Vogue, Vanity Fair, Glamour, Self, GQ, The New Yorker, Condé Nast Traveler/Traveller, Allure, AD, Bon Appétit and Wired, among others.Job Description
Location:
Chennai, TN
Senior/Lead Data Scientist - Recommender Systems & Neural Networks
Condé Nast is a premier media company renowned for producing the highest quality content for the world's most influential audiences, attracting over 100 million consumers across its industry-leading print, digital, and video brands.
Condé Nast is home to many of the world's most-celebrated magazine and website brands. The company's reputation for excellence is the result of our commitment to publishing the best consumer, trade, and lifestyle content. Our brands include Vogue, Epicurious, Vanity Fair, The New Yorker, Wired, and many more. Passion is the core of our philosophy at Condé Nast. Our mission is not only to inform readers but to ignite and nourish their passions.
As a Senior Data Scientist specializing in recommender systems, you will play a key role in designing and implementing state-of-the-art algorithms that power our personalization strategies. You will leverage your expertise in deep learning and neural networks to build scalable, high-impact solutions that enhance user engagement and satisfaction. This role is ideal for someone with a strong technical background and a track record of delivering machine learning products.
You will take a mentorship role within the DS/MLE team and work collaboratively with cross functional teams towards achieving business goals.
RESPONSIBILITIES
Responsibilities include, but are not limited to:
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Lead the design, development, and optimization of cutting-edge recommender systems using advanced neural network techniques. Prior experience on Graph Neural Networks is preferred.
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Analyze user behavior and content data to identify key features that drive personalization and relevance.
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Build, train, and deploy deep learning models tailored for large-scale recommendation tasks, including collaborative filtering, embeddings, and hybrid models.
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Experiment with novel algorithms and techniques to enhance model performance.
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Work with large datasets to extract meaningful features and signals that drive recommendations.
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Collaborate with data engineering teams to define data requirements, pipeline design, and model deployment infrastructure.
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Provide technical leadership and mentorship to junior data scientists, fostering a culture of learning and innovation.
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Guide the team in best practices for machine learning model development, evaluation, and deployment.
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Design and execute A/B tests and online experiments to validate model performance and user impact.
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Analyze experimental results to derive actionable insights and iterate on model improvements.
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Utilize Git for version control, ensuring robust code management and collaboration across the data science team.
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Implement best practices in code review, testing, and documentation to maintain high-quality model development.
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Communicate complex data science concepts and model results effectively to non-technical audiences.
MINIMUM QUALIFICATIONS
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Applicants should have a degree (B.S. or higher) in technical discipline or relevant professional experience
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6-8 years of hands-on experience in data science, with a strong focus on developing recommender systems and neural network models.
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Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch) and deep learning techniques (e.g., CNNs, RNNs, GNNs, Transformers).
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Proficiency in Python (Pandas, NumPy, Scikit-learn) and experience with large-scale data processing (e.g., Spark, Hadoop).
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Strong understanding of recommendation algorithms, including collaborative filtering, content-based filtering, and hybrid approaches.
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Added advantage if the candidate possess knowledge on Graph Neural Networks
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Proven ability to design, implement, and evaluate complex machine learning models in a production environment.
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Experience with machine learning frameworks such as TensorFlow, JAX, PyTorch, Spark MLlib, Keras, or scikit-learn
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Strong communication skills including to non-technical audiences
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Proven attention to detail, critical thinking, and the ability to work both independently and collaboratively within a cross-functional team
What happens next?
If you are interested in this opportunity, please apply below, and we will review your application as soon as possible. You can update your resume or upload a cover letter at any time by accessing your candidate profile.
Condé Nast is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, age, familial status and other legally protected characteristics.
