Senior Data Scientist

Overview

Fanatics

Website: Visit Website

Business Type: Enterprise

Company Type: Product

Business Model: B2C

Funding Stage: Series D+

Industry: Ecommerce

Salary Range: ₹ 35-50 Lacs PA

Job Description

Role Overview

We are looking for a Data Scientist with a strong foundation in Data Engineering who can bridge scalable data systems with advanced machine learning solutions. This role is ideal for professionals who started their journey building robust data pipelines and evolved into deploying impactful ML models.

You will work closely with engineering, product, and analytics teams to design end-to-end data solutions — from ingestion to model deployment.

Key Responsibilities

• Design and build scalable data pipelines (batch & real-time)
• Perform advanced feature engineering on large structured & unstructured datasets
• Develop, train, validate, and deploy machine learning models
• Optimize data workflows using Spark / Airflow / distributed systems
• Collaborate with data engineers to improve data quality and architecture
• Deploy ML models into production environments
• Monitor model performance and continuously improve accuracy
• Work with cloud platforms (AWS / GCP / Azure)
  
  

• Experience building ETL/ELT pipelines
• Expertise in SQL and Python
• Hands-on with Spark / Hadoop ecosystem
• Experience with Airflow or orchestration tools
• Data modeling & warehouse concepts
  
  

• Strong understanding of statistics & ML algorithms
• Experience in model building (regression, classification, clustering)
• Feature engineering expertise
• Model evaluation & optimization
• ML deployment experience
  
  

• AWS / GCP / Azure experience
• Experience with ML deployment frameworks
• Familiarity with Docker / CI-CD is a plus
  
  

• 5–10 years total experience
• Started as Data Engineer and transitioned into Data Science
• Strong problem-solving mindset
• Experience working in product-based organizations preferred
• Ability to own projects end-to-end
  
  

• Experience with real-time streaming systems (Kafka)
• Exposure to MLOps practices
• Experience in large-scale distributed data systems