Data Scientist – Dynamic Pricing & Offer Optimization

Data-Science
March 7, 2026
Remote

Job Overview

  • Date Posted
    March 7, 2026
  • Expiration date
    --

Job 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.

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

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