Shiprocket - Senior Data Scientist - Machine Learning

Senior Data Scientist Shiprocket

Job Overview :

Shiprocket is looking for a highly skilled and experienced Senior Data Scientist to join our dynamic team. As a Senior Data Scientist, you will play a critical role in leveraging data to drive insights and solutions that enhance our logistics platform. You will be responsible for leading data-driven projects, developing predictive models, and working closely with cross-functional teams to optimize operations and improve customer experiences.

In this role, you will also build and scale large-scale machine learning systems, work on GenAI applications including LLMs and RAG pipelines, and lead efforts in fine-tuning models (LoRA, QLoRA, PEFT), MLOps productionization, and vector database integration for real-time :

• Lead Data Science Projects : Oversee the end-to-end execution of data science projects, from data collection and cleaning to model development, validation, and deployment.
• Predictive Modeling : Develop and implement advanced predictive models to solve complex business problems and drive strategic decision-making.
• Data Analysis : Conduct deep-dive analyses to uncover actionable insights and trends that inform business strategies and operations.
• Collaboration : Work closely with product managers, engineers, and other stakeholders to integrate data science solutions into our products and services.
• Innovation : Stay abreast of the latest developments in data science and machine learning, and apply innovative techniques to improve our data capabilities.
• Mentorship : Mentor junior data scientists and data analysts, providing guidance and support to help them grow their skills and contribute effectively to the team.
• Optimization : Continuously monitor and optimize models and algorithms to ensure they remain effective and relevant in a changing business environment.
• ML at Scale : Design and implement large-scale distributed ML systems, including parallel training / inference pipelines across millions of users and transactions.
• LLMs & RAG Pipelines : Build and deploy Retrieval-Augmented Generation pipelines using large language models with custom embedding and retrieval strategies.
• Model Fine-Tuning : Apply techniques such as LoRA, QLoRA, and PEFT for adapting foundation models to domain-specific tasks (e.g., address parsing, fraud scoring).
• Vector Databases : Integrate and optimize vector DBs like FAISS, pgvector, or Milvus for semantic search, retrieval, and matching in LLM workflows.
• MLOps Productionization : Own end-to-end deployment, monitoring, and lifecycle management of ML models using tools like SageMaker, Docker, Airflow, MLflow, or KubeFlow.

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