Data Scientist – Dynamic Pricing & Offer Optimization
TechBiz Global
WorldwideremotePosted 1 month ago
Skill Required
Data-ScienceMachine-LearningPricing-ScienceApplied-SciencesMLOpsDynamic-PricingOffer-OptimizationData-ScientistPricing-Data-ScientistPricing-ScientistMonetization-Data-ScientistR&D-Pricing-ScientistData ScientistMachine LearningScikit-learnStatisticsEngineeringXGBoostTestNGPythonPandasdesignNumPyDesign PatternsandAIFulltime
Key highlights
- Location: Remote
- Department: Data & AI Engineering
- Originally posted on Himalayas
- Level Required: 5–8 years in Applied Machine Learning, Statistical Modeling, and Data Science for large-scale systems
Role overview
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
- Build and deploy models for Churn Propensity / Retention Uplift
- Build and deploy models for Segment Discovery & Similarity (Clustering, KNN)
- Build and deploy models for 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
- 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
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.