Data Scientist – Dynamic Pricing & Offer Optimization

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:

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Build and deploy models for:

Price Elasticity / Conversion Prediction

Churn Propensity / Retention Uplift

Segment Discovery & Similarity (Clustering, KNN)

Offer Recommendation / Ranking (Scoring Models)

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Design A/B testing and uplift modeling to evaluate campaign performance.

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Develop simulation engines for pricing what-if analysis and scenario testing.

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Create automated pipelines for model training, scoring, and retraining.

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Work closely with Data Engineers to ensure feature store alignment.

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Collaborate with the Business Decisioning team to translate insights into rules and thresholds.

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Implement feedback loops using real-time events (purchase, rejection, expiry) to improve models.

Requirements

Required Skills:

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Experience Level: 5–8 years in Applied Machine Learning, Statistical Modeling, and Data Science for large-scale systems

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Strong foundation in Machine Learning, Statistics, and Econometrics.

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Proficient in Python (pandas, scikit-learn, numpy, statsmodels, xgboost, lightGBM).

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Experience with model lifecycle management (MLOps).

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Solid understanding of telecom KPIs: ARPU, recharge frequency, wallet size, churn rate, etc.

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Ability to design feature engineering pipelines and perform A/B testing.

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Expertise in data visualization and storytelling for non-technical stakeholders

Preferred (Nice-to-Have):

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Experience with Telecom Offer & Recharge Modeling or Dynamic Pricing Systems.

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Knowledge of Pricefx PriceAI, Adobe Target Recommendations, or Reinforcement Learning frameworks.

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Understanding of Elasticity Curves, Customer Lifetime Value (CLV), and Offer Fatigue Modeling.

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Experience integrating ML outputs into business decision engines or rule systems.

Highlights

Location: Remote

Department: Data & AI Engineering

Originally posted on Himalayas