Data Scientist

Taraki is hiring for one of its clients.

Location: Remote

Experience Level: 5 to 8 years in Applied Machine Learning, Statistical Modeling, and Data Science for large-scale systems Department: Data & AI Engineering

Compensation: PKR 600,000 to 850,000 (based on experience)

Role Summary:

The Data Scientist will develop ML models to predict customer behavior, price sensitivity, and offer conversion probability. They will build and maintain elasticity models, segmentation models, and reinforcement learning frameworks to enable personalized pricing and offer recommendations.

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

• 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.
  
  
  

Tools & Technologies

Data Science: Python, R, Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch

MLOps: MLflow, SageMaker, Databricks ML, Azure ML Studio

Data: Databricks, Snowflake, S3, Delta Lake

Visualization: PowerBI, Tableau, Streamlit, Dash

Experimentation: Evidently AI, CausalML, upliftML

Versioning: Git, DVC, MLflow Tracking

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.