Data Scientist – Dynamic Pricing & Offer Optimization

Price Elasticity / Conversion Prediction

Churn Propensity / Retention Uplift

Segment Discovery & Similarity (Clustering, KNN)

Offer Recommendation / Ranking (Scoring Models)

• Design A/B testing and uplift modeling to evaluate campaign performance.

• Develop simulation engines for pricing what-if analysis and scenario testing.

• Create automated pipelines for model training, scoring, and retraining.

• Work closely with Data Engineers to ensure feature store alignment.

• Collaborate with the Business Decisioning team to translate insights into rules and thresholds.

• Implement feedback loops using real-time events (purchase, rejection, expiry) to improve models.

• Experience Level: 5-8 years in Applied Machine Learning, Statistical Modeling, and Data Science for large-scale systems

• Strong foundation in Machine Learning, Statistics, and Econometrics.

• Proficient in Python (pandas, scikit-learn, numpy, statsmodels, xgboost, lightGBM).

• Experience with model lifecycle management (MLOps).

• Solid understanding of telecom KPIs: ARPU, recharge frequency, wallet size, churn rate, etc.

• Ability to design feature engineering pipelines and perform A/B testing.

• Expertise in data visualization and storytelling for non-technical stakeholders

• Experience with Telecom Offer & Recharge Modeling or Dynamic Pricing Systems.

• Knowledge of Pricefx PriceAI, Adobe Target Recommendations, or Reinforcement Learning frameworks.

• Understanding of Elasticity Curves, Customer Lifetime Value (CLV), and Offer Fatigue Modeling.

• Experience integrating ML outputs into business decision engines or rule systems.