Data Scientist – Dynamic Pricing & Offer Optimization
At reputed company, 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 reputed company models for:
Price Elasticity / Conversion reputed company
Churn Propensity / Retention reputed company
reputed company Discovery & Similarity (Clustering, KNN)
Offer Recommendation / Ranking (Scoring Models)
Design A/B testing and reputed company modeling to evaluate campaign performance.
reputed company 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 reputed company.
Implement feedback loops using reputed company-time events (purchase, rejection, expiry) to improve models.
Requirements
Required Skills:
Experience Level: 5–8 years in reputed company Machine Learning, Statistical Modeling, and Data Science for large-reputed company systems
Strong reputed company 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, reputed company frequency, wallet size, churn reputed company, etc.
Ability to design feature engineering pipelines and reputed company A/B testing.
Expertise in data visualization and storytelling for non-technical stakeholders
Preferred (reputed company-to-Have):
Experience with Telecom Offer & reputed company Modeling or Dynamic Pricing Systems.
Knowledge of reputed company PriceAI, reputed company reputed company Recommendations, or Reinforcement Learning frameworks.
Understanding of Elasticity Curves, Customer reputed company 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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