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Data Scientist

Job Title: Data Scientist / Machine Learning Engineer
Location: Mumbai (Work from Office – 5 Days)
CTC: ₹21–28 LPA
Experience: 2–5 Years
Industry: Consumer Internet | Product Company

Job Summary

We are hiring a Data Scientist / Machine Learning Engineer to build and scale production-grade ML models for personalization, recommendations, pricing, and fraud detection in a high-traffic consumer internet product.

Key Responsibilities

  • Build and deploy ML models for recommendations, ranking, pricing, fraud, and propensity
  • Work with large structured & unstructured datasets
  • Apply NLP techniques for text classification, embeddings, and user profiling
  • Productionize models using APIs, Docker, CI/CD on AWS/GCP
  • Monitor model performance and business impact

Must-Have Skills

  • 2+ years as a Data Scientist / ML Engineer
  • Strong Python and classical ML fundamentals
  • Hands-on experience in at least 2 ML use cases:
  • Recommendation Systems
  • Image-based ML
  • Fraud / Risk Detection
  • Pricing or Propensity Models
  • Strong exposure to NLP (text classification, embeddings, similarity models)
  • Experience deploying ML models in production
  • Background from B2C product companies only

No Banking / FinTech / Financial Services profiles

Why Join

  • Work on large-scale, real-world ML problems
  • High ownership and end-to-end impact
  • Competitive pay and strong growth opportunities

Apply with your updated CV

Keywords: Data Scientist, Machine Learning Engineer, Python, NLP, Recommendation Systems, AWS, GCP, Product Company, Mumbai

Job Type: Full-time

Pay: ₹2,100,000.00 - ₹2,800,000.00 per year

Benefits:

  • Provident Fund

Application Question(s):

  • What's your current company?
  • Which use cases you have hands on experience?
  • Are you ok for Mumbai location (if candidate is from outside Mumbai)?
  • Reason for change (if candidate has been in current company for less than 1 year)?

*
Reason for hike (if greater than 25%)?

  • Current ctc nd notice period??

Work Location: In person

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