Senior Data Scientist

SourcingXPress; Hyderabad; On-site; INR 3,500,000 - 5,000,000; Full time

Job summary

A prominent technology firm is seeking a skilled Data Scientist with a strong Data Engineering background in Hyderabad, India. This role involves designing and building scalable data pipelines, performing feature engineering on datasets, and developing machine learning models. You should have expertise in SQL, Python, and experience with cloud platforms like AWS, GCP, or Azure. Ideal candidates will have 5-10 years of total experience, with a strong problem-solving mindset and the ability to manage projects end-to-end.

Qualifications

• 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.

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).

Skills

Strong Data Engineering Background

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

Tools

Spark

Hadoop

Airflow

Docker

Job description

Company

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

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)

Required Skills

• Strong Data Engineering Background
• 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

Ideal Candidate

• 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

Good to Have

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