Data Scientist – Dynamic Pricing & Offer Optimization
At a glance
Mid-level Data Science role at TechBiz Global GmbH. Remote, Pakistan · full-time.
Pay not stated Data Scientist salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and deploy models for price response, conversion, churn, retention, customer segments and offer rankings
- Test campaign effects with A/B experiments and uplift models, then simulate pricing scenarios
- Automate model training, scoring and retraining, keeping features aligned with data engineering
- Turn model insights into decision rules with business teams, then use purchase and offer events to improve predictions
What you bring
- Five to eight years applying machine learning, statistical modeling and data science to large-scale systems
- Strong grounding in machine learning, statistics and econometrics, with Python proficiency across the listed libraries
- Experience managing model lifecycles and building feature pipelines, plus the ability to run A/B tests
- Understanding of telecom measures such as ARPU, recharge frequency and churn, and skill in explaining data to non-technical audiences
Who this fits
This mid-level role suits a data scientist with applied modeling experience who can work across model development, experimentation and business decision rules. It is remote and based in Islamabad, Pakistan.
From the employer
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.
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.
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