Data Scientist - Dynamic Pricing & Offer Optimization

About

At TechBiz Global, we are providing recruitment service to our TOP clients from our portfolio. We are currently seeking a Data Scientist to join one of our clients ' teams. If you're looking for an exciting opportunity to grow in a innovative environment, this could be the perfect fit for you.

Responsibilities

• Build and deploy models for:
  • 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.

Requirements

Required Skills

• 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

Preferred (Nice-to-Have)

• 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.