The Data Scientist, will build, enhance, and maintain advanced analytical and machine‑learning models that inform global commercial decisions.
This role contributes directly to pricing strategy, deal guidance, margin improvement, and commercial analytics adoption across the organization.
Working closely with Sales, Finance, Pricing, IT, and Commercial Excellence, the Data Scientist will design predictive and prescriptive models, monitor business performance, translate analytical insights into actionable recommendations, and ensure models remain explainable, robust, and decision‑ready.
The position requires strong technical skills, a pragmatic approach to solving complex problems, and the ability to transform imperfect commercial data into meaningful insights. Curiosity, ownership, and a desire to explore modern tools and methodologies—including LLM‑based workflows—are key success factors.
- Maintain, enhance, and scale machine‑learning models used by global commercial teams.
- Design new predictive and prescriptive models (e.g., deal scoring, churn, price uplift, segmentation, error detection).
- Conduct quantitative analytics, including model monitoring, performance diagnostics, and continuous improvement.
- Translate commercial and pricing needs into robust, scalable analytical models.
- Partner closely with cross‑functional teams (Sales, Finance, Pricing, IT) to support model deployment and adoption.
- Ensure analytical outputs are explainable, accurate, and ready to support decisions.
- Bring structure and consistency to complex, fragmented commercial datasets.
- Monitor model outcomes and continuously refine them based on real‑world performance.
- Systematically reassess the relevance of existing models as business needs evolve.
Champion pragmatic innovation and data‑driven decision‑making across the organization.
Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative field. A PhD is a plus.
More than 5 years of experience in data science, advanced analytics, or machine‑learning roles supporting commercial, pricing, or financial use cases.
Strong skills in ML modeling, feature engineering, and model evaluation.
Proficiency in Python, SQL, and cloud environments
Experience with Databricks, Azure ML, and scalable data pipelines.
Solid understanding of pricing analytics, commercial performance, and margin optimization.
Ability to translate business needs into analytical models and communicate insights clearly.
Experience in cross‑functional collaboration with global teams.