Senior Data Scientist / Senior Machine Learning Engineer

About the Role

Seeking a Data Scientist/Machine Learning Engineer specializing in Recommendation Systems to build scalable models and enhance user experience. Responsibilities include end-to-end project ownership and cross-functional collaboration.

You will move beyond simple analysis to research, build, and deploy production-grade models that power discovery for millions of users.

In this role, you will bridge the gap between research and engineering. You will design advanced architectures and ensure they run efficiently at scale. You will work closely with our engineering teams to turn millions of data points into experiences that anticipate user intent in real-time.

Responsibilities

• Build End-to-End Recommendation Pipelines: Design and implement scalable recommendation architectures to surface relevant content from large catalogs.
• Full-Cycle Project Ownership: Take ownership of projects across the complete Machine Learning lifecycle, driving initiatives from initial problem formulation and exploratory analysis to model training, validation, and post-deployment monitoring.
• Advanced Behavioral Modeling: Develop and train deep learning models (e.g., GNNs, Transformers, Wide-to-Narrow networks) to create rich user and item embeddings based on interactions.
• Scalable Multi-GPU Training: Design and execute distributed training workflows for large-scale deep learning models, utilizing multi-GPU strategies and parallel computing techniques.
• Strategic Signal Extraction & Feature Modeling: Systematically mine and sift through high-dimensional data to distinguish between subtle implicit signals and explicit feedback, and model these behaviors to dense, predictive features that enhance model performance.
• Production Engineering: Write clean, production-ready code (Python) and oversee the deployment of models into high-availability environments. You will optimize models for low latency to ensure instant load times.
• Cross-Functional Collaboration: Partner closely with Product and Engineering teams to translate business requirements into technical specifications, ensuring seamless development, integration, and deployment of models into the core product ecosystem.

Requirements

• Experience: 6+ years of overall professional experience, including at least 2+ years in Recommendation Systems.
• Technical Skills: Strong proficiency in Python and hands-on experience with ML frameworks such as PyTorch and TensorFlow.
• Mathematical Foundation: Deep understanding of advanced statistics, matrix factorization techniques, and probability theory.
• Deep Learning: Practical expertise in modern machine learning and deep learning approaches.
• Production Readiness: Proven ability to write scalable, production-grade code and design systems that handle large-scale data.
• Product Mindset: A strong focus on solving real-world product challenges—like churn reduction and session continuity—through data-driven solutions.
• MLOps: Experience with MLOps practices and CI/CD automation pipelines.

Bonus Points

• Experience in UGC or PGC platforms.
• Experience with multi-lingual content processing.
• Experience with Agentic AI or semantic search technologies.

Non-Negotiables

• High ownership and accountability
• Strong written and verbal communication skills
• Comfort with ambiguity and experimentation
• Bias toward action, measurable outcomes

Security & Data Handling Responsibilities: Handle sensitive data responsibly in compliance with the DPDP Act, ISO-27001:2022, and Pratilipi's internal security policies — ensuring data privacy, confidentiality, and NDA obligations at all times.