Senior Data Scientist, Deep Recommender Systems

Couture; Bengaluru; On-site; INR 1,500,000 - 2,000,000; Full time

Job summary

A tech-driven company in Bengaluru is seeking a Senior Data Scientist to lead the development of deep learning recommender systems. The ideal candidate will have hands-on experience in building and optimizing large-scale systems, alongside a strong proficiency in Python and various deep learning frameworks. The role emphasizes collaboration with cross-functional teams and a commitment to best practices in MLOps, making it critical for driving personalized user recommendations and contributing to business success.

Qualifications

• Experience building, deploying, and maintaining deep learning recommender systems.
• Understanding of architectures like Two-Tower and Collaborative Filtering.
• Proficiency in Python and data science libraries like Pandas and NumPy.

Responsibilities

• Lead development of deep learning recommender systems from ideation to deployment.
• Collaborate with cross-functional teams for scalable recommendation pipelines.
• Design and execute A/B tests to measure business impact.

Skills

Deep learning recommender systems

Python

A/B testing

Collaborative Filtering

Graph Neural Networks

Education

M.S. or Ph.D. in a quantitative field

Tools

TorchRec

TensorFlow Recommenders

NVIDIA Merlin

Spark

Job description

Join our mission to deliver AI infrastructure at scale. Explore opportunities to work with advanced middleware tech stacks.

Senior Data Scientist, Deep Recommender Systems

Lead design and deployment of deep learning recommender systems.

On-site

Full-Time

2025-11-07

About Couture AI

Couture AI provides cutting‑edge pluggable building blocks for the entire AI stack. These components enable enterprises to build and scale machine learning (ML) and deep learning (DL) solutions, addressing diverse use cases. Our platform is utilized by some of the world's largest organizations to deploy vertical‑specific products built on our proprietary AI infrastructure.

Job Description

We are seeking a seasoned and hands‑on Senior Data Scientist to lead the design, development, and deployment of our next‑generation deep learning recommender systems. The ideal candidate will be a technical expert with a proven track record of building and optimizing large‑scale recommender systems in a production environment. You will play a pivotal role in driving our business metrics by delivering highly personalized and relevant recommendations to our users.

Key Responsibilities

• Lead the end‑to‑end development of deep learning‑based recommender systems, from ideation and research to production deployment and monitoring.
• Design and implement multi‑stage recommender system architectures, including efficient retrieval (candidate generation) and sophisticated ranking models.
• Develop and optimize deep learning models for recommendations using architectures such as Two‑Tower, Wide & Deep, and Transformer‑based models.
• Manage and create large‑scale embedding layers for categorical features related to both users and products to effectively represent high‑dimensional data.
• Utilize and evaluate various specialized libraries and frameworks like TorchRec, TensorFlow Recommenders (TFRS), Microsoft Rec, Alibaba RecSys, and NVIDIA Merlin to accelerate development and deployment.
• Work with large, sparse datasets and tackle unique challenges in recommender systems, such as the cold‑start problem and the long‑tail problem.
• Collaborate with data engineers, machine learning engineers, and product managers to ensure the entire recommendation pipeline is scalable, robust, and aligned with business goals.
• Design and execute A/B tests to rigorously measure the business impact of new models and features.
• Implement MLOps best practices for continuous integration, deployment, and monitoring of recommender systems in a production setting.
• Mentor junior data scientists and contribute to a culture of technical excellence and innovation.

Required Qualifications

• Hands‑on experience building, deploying, and maintaining deep learning recommender systems at scale.
• Deep understanding and practical experience with architectures such as Two‑Tower, Wide & Deep, Session‑based and Sequential, Collaborative Filtering (e.g., Neural Collaborative Filtering), Graph Neural Networks, and embedding layers for high‑dimensional categorical features.
• Hands‑on experience with at least two of the following libraries or frameworks: TorchRec, TensorFlow Recommenders (TFRS), NVIDIA Merlin.
• Demonstrated ability to build and deploy systems that handle terabytes of data with low latency, including distributed training and real‑time serving.
• Strong track record of solving common recommender system challenges such as cold‑start and long‑tail problems.
• Proficiency in experimental design and A/B testing methodologies.
• Expert‑level proficiency in Python and its data science ecosystem (Pandas, NumPy).

Preferred Qualifications

• Advanced degree (M.S. or Ph.D.) in a quantitative field such as Computer Science, Statistics, or a related discipline.
• 3 years of experience building recommendation systems at scale.
• Experience with big data technologies (e.g., Spark, HDFS, Databricks) and vector databases.
• Familiarity with other deep learning architectures for sequence modeling (e.g., Transformers).

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