Data Scientist (Risk-Based Pricing)

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.