| Описание: |
About the Project
Fuel card sales in the U.S. (all sales are conducted within the United States).
Project launch: March 2024.
Part of a logistics group: The project is a division of a U.S. trucking logistics group, which is the market leader in Uzbekistan.
The company is a registered IT Park resident with offices in Tashkent (two offices), Chicago, and Orlando.
Purpose of the Role
The main goal of this role is to design and implement a set of risk-based pricing models that determine individual fuel discounts ($/gallon) for customers based on 20–30 financial, behavioral, and industry-related factors. Models should cover new, existing, and churn-risk clients, with a clear business impact evaluation.
Key Responsibilities -
Analyze and clean large historical datasets (2–3 GB in Excel format). -
Design and implement multiple pricing models tailored to different client categories. -
Perform feature engineering and variable selection (20–30 features: finance, behavior, industry, etc.). -
Train and calibrate models using algorithms such as LightGBM, XGBoost, Logistic Regression. -
Build explainable models with SHAP, feature importance, and other interpretability tools. -
Develop a framework for business-effect evaluation (uplift, sensitivity analysis). -
Prepare models for use by the finance department and potential automation via API. -
Document hypotheses, model logic, feature selection, and interpretations. -
Provide recommendations for deployment (batch scoring, API integration, model updating). -
Plan quarterly model recalibration and monitoring. Requirements -
3–5+ years of hands-on experience in Data Science or Applied Machine Learning. -
Proven expertise in scoring, risk, or pricing models. -
Strong Python skills (pandas, scikit-learn, XGBoost/LightGBM). -
Experience in feature engineering and explainable modeling (e.g., SHAP). -
Understanding of pricing logic, discounting mechanisms, and sensitivity analysis. -
Ability to work with large Excel datasets and extract insights. -
Strong independence in managing the full cycle: from analysis to implementation recommendations. Nice to Have -
Background in fintech, e-commerce, or dynamic pricing systems. -
Experience deploying ML models (FastAPI, Docker, MLflow). -
Knowledge of scorecard model development. -
Experience with visualization tools (Plotly, Streamlit). Technologies & Tools -
Python (pandas, scikit-learn, XGBoost, LightGBM, SHAP) -
Excel, Jupyter, SQL (optional) -
MLflow, Streamlit (when needed) -
FastAPI (for production deployment if required) What We Offer -
Competitive compensation (discussed individually based on competencies). -
Direct access to company leadership – your expertise and ideas will be valued. -
5/2 schedule following the U.S. production calendar for holidays and weekends. -
Working hours: 18:00–02:00 (Tashkent time). -
Office-based position in Tashkent. |
