- Company Name
- XPO
- Job Title
- Senior Scientist, Data Science - Hybrid
- Job Description
-
**Job title**
Senior Scientist, Data Science – Hybrid
**Role Summary**
Lead the design, development, and deployment of AI, machine learning, and optimization solutions to drive revenue, pricing, demand forecasting, and financial performance across the organization. Shape the data science practice by integrating cutting‑edge research, rigorous statistical methodology, and production‑grade engineering. Collaborate with cross‑functional teams and communicate insights to senior leadership.
**Expactations**
- Deliver AI‑enabled insights that directly improve operations, financial outcomes, or customer experience.
- Build and deploy production‑ready models, ensuring scalability, reliability, and robustness.
- Serve as a thought leader and subject‑matter expert in advanced AI techniques, particularly for pricing and logistics.
- Translate complex analytical findings into clear business recommendations for technical and non‑technical audiences.
- Continuously research, evaluate, and implement emerging AI methods to maintain a competitive advantage.
**Key Responsibilities**
- Develop and production‑grade machine learning, deep learning, and reinforcement learning models for pricing optimization, demand forecasting, and consumer behavior.
- Apply causal inference and measurement techniques to assess the impact of pricing algorithms on revenue.
- Conduct statistical modeling, hypothesis testing, anomaly detection, and model validation.
- Design, test, and implement optimization solutions that integrate with in‑house software platforms.
- Prepare and present ad‑hoc and strategic analyses to senior leaders.
- Collaborate with data engineers and software teams to deploy models, monitor performance, and iterate.
- Drive process improvements through analytics and automation, aligning with the organization’s AI maturity.
**Required Skills**
- Programming: Python, R, SQL, SAS, Spark, Java, or equivalent; strong code quality and documentation.
- Machine learning: supervised, unsupervised, deep learning, and reinforcement learning.
- Optimization: linear, integer, and stochastic programming, pricing and revenue optimization.
- Statistical analysis: hypothesis testing, anomaly detection, inference, model validation.
- Causal inference and impact measurement.
- Analytics pipeline: data preprocessing, feature engineering, model training, deployment, monitoring.
- Communication: ability to explain technical concepts to business stakeholders and lead presentations.
- Collaboration: experience working with cross‑functional teams including data scientists, engineers, and product managers.
**Required Education & Certifications**
- Bachelor’s in Computer Science, Statistics, Mathematics, Engineering, Economics, or related quantitative field.
- Master’s or Ph.D. in Computer Science, Data Science, AI, or a related quantitative discipline is preferred.
- 5+ years of hands‑on experience in pricing optimization or demand forecasting, including reinforcement learning and causal inference.
- Proven track record of deploying AI models and delivering measurable business impact.