Commercial Pricing Analyst

A massive entertainment company is looking for a Commercial Pricing Manager to support a major commercial transformation focused on pricing in a digital commerce environment. This is a contingent role for someone who can operate at pace, bring structure to ambiguity, and turn data, customer insight, commercial performance, and analytical recommendations into clear business decisions. It is a hands-on, data-heavy role, not a coordination role. The role sits at the intersection of commercial strategy, e-commerce pricing, analytics, measurement, AI/ML-enabled pricing tools, merchandising, operations, and business adoption.

You will help teams understand what the data is saying, what decisions need to be made, what trade-offs matter, and how pricing actions can be executed with discipline and measurable impact. The right person will combine consulting-style problem solving with hands-on analytical capability. You should be comfortable working with incomplete information, challenging assumptions constructively, and producing high-quality recommendations quickly.

What You Will Do

• Support commercial pricing decisions by analyzing performance, customer behavior, commercial trends, pricing recommendations, and business impact.
• Analyze e-commerce performance drivers such as conversion, traffic, customer segments, product or category mix, promotional activity, basket behavior, revenue, margin, and customer response.
• Perform hands-on commercial analysis using SQL, Tableau or similar BI platforms, spreadsheets, and agentic AI tools such as Codex.
• Translate complex analysis into clear recommendations, including options, trade-offs, risks, assumptions, and next steps.
• Pressure-test pricing recommendations from a commercial perspective, including customer impact, operational feasibility, revenue opportunity, adoption risk, and execution readiness.
• Partner with analytics, measurement, data, product, merchandising, finance, and operations teams to improve decision quality and business confidence.
• Translate commercial questions into clear analytical requirements for data science, ML, and central analytics teams, including metric definitions, data cuts, hypotheses, guardrails, and acceptance criteria.
• Work with ML teams on AI-driven pricing tools by helping define business requirements, validate model outputs, identify commercial edge cases, and translate recommendations into practical business actions.
• Help define and interpret success measures, including revenue impact, adoption, customer response, operational execution, and business scalability.
• Build practical decision packs, performance readouts, and senior stakeholder materials that enable faster and better commercial decisions.
• Identify where additional evidence, stakeholder alignment, or business guardrails are needed before pricing actions are taken.
• Support business teams in adopting pricing recommendations consistently and turning learning into repeatable ways of working.

Early Focus

In the initial period, the role is expected to build context quickly, understand current pricing transformation priorities, identify where better analysis or decision support is needed, and help create clearer recommendations for business stakeholders. Over time, the role should help improve the quality, speed, and confidence of pricing decisions by strengthening analytical interpretation, stakeholder alignment, and repeatable ways of working.

What You Will Bring

• Experience in e-commerce, digital commerce, D2C, marketplace, subscriptions, commercial pricing, commercial analytics, revenue management, consulting, commercial strategy, growth analytics, or a related field.
• Strong understanding of e-commerce commercial levers, including pricing, promotions, conversion, funnel performance, product or category performance, merchandising, customer segmentation, revenue, margin, and customer experience.
• Strong hands-on analytical capability, including advanced use of SQL, Tableau or similar BI platforms, spreadsheets, dashboards, and large commercial datasets.
• Strong ability to use agentic AI tools such as Codex to accelerate analysis, synthesis, requirement drafting, documentation, and stakeholder communication.
• Experience working with ML, data science, or advanced analytics teams on AI-driven pricing tools, pricing recommendation engines, model-led decisioning, or similar commercial analytics products.
• Ability to translate data into practical business recommendations, not just produce analysis.
• Ability to define analytical requirements for data science, ML, and central analytics teams, including the commercial question, required data, metrics, hypotheses, expected outputs, and business decision to support.
• Consulting-style problem solving: able to structure ambiguous problems quickly, work at pace, and produce high-quality outputs for senior stakeholders.
• Strong commercial judgment, including understanding of revenue, margin, conversion, offer performance, customer impact, adoption, and operational execution.
• Practical understanding of experimentation and measurement, including A/B testing, incrementality, confidence, bias, and the limits of simple before-and-after comparisons.