Data Scientist – Dynamic Pricing & Offer Optimization

Description

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.

Key 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.

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.