Yes Madam - Senior Data Scientist

YesMadam

Noida

5 - 8 Years

4.5

94+ Reviews

Data Science Data Scientist Predictive Analytics NLP Fraud Analytics SQL Python Time Series Forecasting Deep Learning

Posted on: 20/11/2025

Job Description

Company : Yes Madam Beauty & Wellness at Home

Location : Noida Sector-63

Role Type : Full-Time

Experience : 5+Years

Role & Responsibilities :

Customer Intelligence & Growth :

- Build churn prediction, customer segmentation, recommendation systems, and CLTV prediction models.

- Develop next-best-service, upsell/cross-sell, and campaign response models.

- Analyse customer behaviour to improve rebooking, retention and frequency across beauty categories.

Marketing Science & Pricing :

- Build dynamic pricing, discount optimisation, and propensity-to-buy models.

- Perform market basket analysis and optimise service bundles.

Operations & Supply Optimisation :

- Create demand forecasting models for city/area/category.

- Optimise beautician routing, slot availability, and travel time prediction.

- Forecast consumables and inventory usage.

Quality & Experience :

- Use NLP for review analysis, sentiment classification, and topic modelling.

- Build rating prediction models and quality-alert systems.

Fraud Detection & Safety :

- Build anomaly detection models for coupon misuse, fake bookings, and suspicious customer/beautician behaviour.

- Support verification models such as face matching and OCR-based checks.

Product Analytics & BI :

- Run A/B tests, causal inference, and support data-driven decision making.

- Build dashboards for leadership on demand, cohorts, utilisation, cancellations, and fulfilment.

- Present clear insights and recommendations.

Skills Required :

- Strong expertise in Python, SQL, Pandas, NumPy, Scikit-learn, XGBoost/LightGBM, Time-Series Models, NLP, and Deep Learning basics.

- Experience with LSTM/GRU, ARIMA, Prophet, BERT, clustering algorithms, market basket analysis, and anomaly detection models.

- End-to-end experience in model building, validation, deployment, and working with large datasets.

- Ability to translate data into actionable insights for business and operations teams.