Data Scientist – Pricing & Revenue Optimization

DHL Regional Services, Inc.; Plantation (FL); On-site; USD 110,000 - 170,000; Full time

Job summary

DHL Regional Services, Inc. is seeking a Data Scientist for Pricing & Revenue Optimization to drive profitable growth through advanced analytics and experimentation.

You will build WTP models, optimize revenue and margins across customer segments, and support pricing decisions with rigorous data-driven insights. Responsibilities include deploying ML models, running pricing experiments (A/B and elasticity tests), and collaborating with Sales, Finance, Operations and IT to translate analytics into

Qualifications

• Proven ability to deploy predictive models in production.
• Strong background in pricing/revenue management.
• Experience with commercial or pricing teams is desirable.
• Experience in customer segmentation and behavioral analytics.

Responsibilities

• Develop and deploy willingness-to-pay models using machine learning.
• Design optimization models to balance volume growth and margin.
• Develop predictive churn and retention models.
• Design pricing experiments (A/B testing, elasticity testing).
• Collaborate with IT and data engineering to deploy models to production.
• Translate insights to cross-functional actions with Sales, Finance, Ops and Marketing.

Skills

Python

R

SQL

Pandas/NumPy/scikit-learn

Machine learning

Optimization

Time-series forecasting

A/B testing

Elasticity modeling

Big data tools (Spark)

Cloud environments

Pricing strategy

Education

Bachelor’s degree in Data Science, Statistics, Economics, Engineering, Mathematics, or related field

Tools

Spark

Cloud platforms (AWS/GCP)

Job description

Data Scientist — Pricing & Revenue Optimization

Role Overview

We are seeking a highly analytical and commercially driven Data Scientist to join our Regional Pricing & Products team . This role is critical to driving profitable growth, yield optimization, and customer retention through advanced analytics, machine learning, and experimentation.
You will support the design and deployment of pricing models that enhance willingness-to-pay estimation , optimize customer lifetime value , and proactively identify risks to revenue and margin across customer segments. This position requires a strong combination of data science expertise, business acumen, and the ability to translate insights into actionable pricing strategies.

Key Responsibilities

1. Pricing & Willingness-to-Pay Modeling

• Develop and enhance willingness-to-pay (WTP) models using machine learning techniques.
• Analyze customer behavior, shipment characteristics, and competitive dynamics to improve pricing precision.
• Build segmentation-based pricing strategies to maximize yield while maintaining competitiveness.

2. Revenue Growth & Yield Optimization

• Design optimization models to balance volume growth vs. margin expansion.
• Implement dynamic pricing strategies tailored to customer segments and product lines.

3. Customer Retention & Churn Reduction

• Develop predictive models to identify churn risk and retention opportunities.
• Design and evaluate pricing experiments (A/B testing, elasticity testing) to improve customer stickiness.

4. Predictive Analytics & Risk Identification

• Analyze trends and forecast customer-level and segment-level revenue patterns.
• Identify early warning signals of top-line and bottom-line risks.
• Propose data-driven mitigation strategies and commercial actions.

5. Experimentation & Model Deployment

• Build and manage pricing experimentation frameworks.
• Collaborate with IT and data engineering to deploy models into production environments.

6. Stakeholder Collaboration

• Partner with Sales, Pricing, Finance, Operations and Marketing teams to translate insights into action.
• Communicate complex analytical findings to non-technical stakeholders effectively.

Required Skills

Technical Skills

• Strong expertise in:
• Python or R (pandas, NumPy, scikit-learn, etc.)
• SQL for large-scale data extraction and transformation
• Experience with:
• Machine learning models (regression, classification, clustering)
• Optimization techniques (linear programming, pricing optimization)
• Time-series forecasting
• Knowledge of:
• A/B testing and experimentation design
• Elasticity modeling and demand forecasting
• Familiarity with big data tools (e.g., Spark) and cloud environments is a plus

Analytical & Business Skills

• Strong understanding of pricing strategy and revenue management principles
• Ability to connect modeling outputs to commercial outcomes
• Experience in customer segmentation and behavioral analytics

Soft Skills

• Excellent communication and storytelling skills with data
• Ability to influence senior stakeholders
• Strong collaboration in cross-functional, global teams
• High level of ownership and results orientation

Experience & Qualifications

• Bachelor’s degree in Data Science, Statistics, Economics, Engineering, Mathematics, or related field
• 4–8 years of experience in data science, preferably in:
• Pricing / revenue management
• Logistics, transportation, airlines, or e-commerce industries
• Proven track record of:
• Deploying predictive models in production
• Driving measurable business impact (revenue growth, margin improvement, retention)
• Experience working with commercial or pricing teams is highly desirable