Data Science
Python
Machine Learning (ML)
Docker
Amazon Web Services (AWS)Review Criteria
- Strong Data Scientist/Machine Learnings/ AI Engineer Profile
- 2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML models
- Strong expertise in Python with the ability to implement classical ML algorithms including linear regression, logistic regression, decision trees, gradient boosting, etc.
- Hands-on experience in minimum 2+ usecaseds out of recommendation systems, image data, fraud/risk detection, price modelling, propensity models
- Strong exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured text
- Experience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environments
- Preferred (Company) – Must be from product companies
Job Specific Criteria
- CV Attachment is mandatory
- 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%)?
Role & Responsibilities
- Partner with Product to spot high-leverage ML opportunities 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
- 2–5 years of DS/ML experience in consumer internet / B2C products, with 7–8 models shipped to production end-to-end.
- Proven, hands-on success in at least two (preferably 3–4) of the following:
- Recommender systems (retrieval + ranking, NDCG/Recall, online lift; bandits a plus)
- Fraud/risk detection (severe class imbalance, PR-AUC)
- Pricing models (elasticity, demand curves, margin vs. win-rate trade-offs, guardrails/simulation)
- Propensity models (payment/churn)
- Programming: strong Python and SQL; solid git, Docker, CI/CD.
- Cloud and data: experience with AWS or GCP; familiarity with warehouses/dashboards (Redshift/BigQuery, Looker/Tableau).
- ML breadth: recommender systems, NLP or user profiling, anomaly detection.
- Communication: clear storytelling with data; can align stakeholders and drive decisions.
Shubham Vishwakarma
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Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon