Roles & Responsibilities
Partner with Product to spot high-leverage ML prospects tied to business metrics.
Wrangle large structured and unstructured datasets; build reliable features and data contracts.
Build and ship models to:
Enhance customer experiences and personalization
Boost revenue via pricing/discount optimization
Power user-to-user discovery and ranking (matchmaking at scale)
Detect and block fraud/risk in real time
Score conversion/churn/acceptance propensity for targeted actions
Collaborate with Engineering to productionize via APIs/CI/CD/Docker on AWS.
Design and run A/B tests with guardrails.
Build monitoring for model/data drift and business KPIs

Ideal Candidate
Solid Data Scientist/Machine Learnings/ AI Engineer Profile
Mandatory (Experience 1) – Must have 3+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML models
Mandatory (Experience 2) – Must have strong expertise in Python with the ability to implement classical ML algorithms including linear regression, logistic regression,



decision trees, gradient boosting, etc.
Mandatory (Experience 3): Must have practical hands-on experience with Neural Network / Deep Learning models (TensorFlow / PyTorch preferred)
Mandatory (Experience 4) – Must have hands-on experience in minimum 2+ usecaseds out of recommendation systems, image data, fraud/risk detection, price modelling, propensity models
Mandatory (Experience 5) – Must have robust exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured text
Mandatory (Experience 6) – Must have experience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environments
Mandatory (Company) – Must be from product companies, Avoid candidates from financial domains (e.g., JPMorgan, banks, fintech)
Mandatory (Note): The candidate must currently be based in M

📌 Data Scientist Mumbai (India)
🏢 CGvak Software Exports
📍 India