Posted today · be early

Data Scientist – Dynamic Pricing & Offer Optimization

TechBiz Global

WorldwideremotePosted 1 day 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

Remote work location
5–8 years of applied machine‑learning experience required
Department: Data & AI Engineering
Strong focus on telecom KPIs such as ARPU and churn rate
Requires proficiency in Python and MLOps
Involves building pricing, churn, and recommendation models

Role overview

TechBiz Global is recruiting for a Data Scientist to join a client’s team, working in an innovative environment to build and deploy advanced machine‑learning models that drive pricing, churn, segmentation, and offer recommendation. The role involves close collaboration with data engineers and business decision teams to operationalize models, run A/B tests, and create simulation engines for what‑if pricing analysis.

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

5–8 years experience 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