Lead Commercial Data Scientist

• Build and productionise predictive deal-scoring models that analyze historical win rates, engagement data, and market triggers to rank inbound leads for and White Space opportunities for Sales.
• Develop “Next-Best-Action” recommendation algorithms integrated directly into Salesforce, providing Account Executives with automated, real-time prompts for the highest-value upsell and cross-sell angles.
• Architect automated account health dashboards that translate complex institutional API and software usage patterns into scannable sales alerts, flagging high-risk client contraction or accounts primed for expansion.
• Engineer white-space & TAM analysis models to systematically audit the global commodity market, identifying untapped logos and potential enterprise customers currently missing from our pipeline.

Pricing Algorithm Optimization & Revenue Defense

• Design dynamic price elasticity and optimization models that simulate client price tolerance across various customer segments to maximize ARR during annual renewals.
• Develop contract value simulation matrices that arm Account Executives with data-backed parameters for high-stakes enterprise negotiations, protecting pricing boundaries.
• Quantify the specific commercial revenue upside of raw feature updates or new energy index methodologies, telling product and sales leaders exactly how to monetize new data assets.

Cross-Functional Sales Enablement & Engineering

• Partner directly with Sales/Revenue Operations and Data Engineering to build, maintain, and clean automated sales data pipelines within cloud environments (e.g., Snowflake, Databricks).
• Translate highly intricate mathematical models into intuitive, low-jargon dashboards, training global commercial teams to trust and execute on data-driven sales leads.
• Establish rigorous data quality loops ensuring that automated alerts pushed to the sales floor are completely accurate and actionable, directly maintaining trust in internal forecasting tools.

• Education : Master’s or Ph.D. in Data Science, Quantitative Finance, Statistics, Economics, Business Analytics, or a closely related quantitative field.
• Experience : 5+ years of practical data science experience, with a heavy emphasis on sales intelligence, revenue analytics, or go-to-market data science inside a B2B SaaS, FinTech, or Price Reporting Agency (PRA) setting.
• Technical Stack : Advanced mastery of Python or R, production-grade SQL, and deep experience linking machine learning workflows to Salesforce CRM via automated APIs .
• Methodology Expertise : Proven skill in supervised classification (lead scoring), predictive churn forecasting, customer segmentation (clustering), and value-based price optimization modeling.
• Domain Knowledge : High comfort with the enterprise sales funnel (pipelines, conversion rates, ARR, net revenue retention) alongside an interest in physical energy and chemical supply chains.

Success Profile

• Sales-First Mindset : Driven by the thrill of closed deals and absolute pipeline growth, seeing math as the ultimate tool to unlock hidden commercial revenue.
• Elite Communicator & Collaborator : Able to collaborate effectively in a matrix environment and to stand in front of a global sales floor or senior revenue executives and explain advanced data science models using simple, highly motivating language.
• Fast-Paced Operator : Comfortable deploying iterations quickly, prioritizing rapid sales-enablement wins without sacrificing the absolute integrity of the underlying code.