Data Scientist – Dynamic Pricing & Offer Optimization

Requirements

• 5-8 years in Applied Machine Learning and Data Science for large-scale systemsMust
• Strong foundation in Machine Learning, Statistics, and EconometricsMust
• Python proficiency (pandas, scikit-learn, numpy, statsmodels, xgboost, lightGBM)Must
• Model lifecycle management (MLOps)Must
• Telecom KPI knowledge (ARPU, recharge frequency, wallet size, churn rate)Must
• Telecom Offer and Recharge Modeling experience
• Dynamic Pricing Systems experience
• Elasticity Curves and Customer Lifetime Value (CLV) modeling
• Offer Fatigue Modeling

What you'll do

• Build and deploy predictive models for price elasticity, conversion, churn propensity, and retention
• Design and implement A/B testing and uplift modeling frameworks
• Develop simulation engines for pricing what-if analysis and scenario testing
• Create automated pipelines for model training, scoring, and retraining
• Design feature engineering pipelines
• Implement clustering and similarity models (clustering, KNN, segment discovery)
• Build offer recommendation and ranking scoring models
• Implement feedback loops using real-time events to improve models
• Data visualization and storytelling for non-technical stakeholders
• Collaborate with Data Engineers to ensure feature store alignment
• Work with Business Decisioning team to translate insights into rules and thresholds
• Integrating ML outputs into business decision engines or rule systems

Keywords

About the role

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. 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. Highlights Location: Remote Department: Data & AI Engineering Originally posted on Himalayas