Data Scientist - Pricing and Inventory

Location: Manchester (Hybrid - 2 days onsite)Salary: £60,000-£90,000

A global content powerhouse is building a next-generation analytics hub, reshaping how millions interact with digital media across publishing, audio, and out-of-home platforms. With data at the core of their strategic direction, they are expanding their team and hiring a Data Scientist to focus on optimising one of their largest revenue streams-advertising inventory.

As a Data Scientist - Inventory & Pricing Analytics, you will play a pivotal role in optimising the company's advertising inventory, pricing models, and revenue performance. You will work across datasets covering sales activity, inventory levels, supplier lead times, and customer demand to develop predictive models that inform effective pricing and stock management strategies.

This role demands both technical acumen and commercial awareness, as you'll directly impact profitability through smarter pricing, stock optimisation, and predictive inventory management.

• Develop predictive and statistical models to optimise inventory management, pricing strategies, and advertising sales performance.

• Analyse inventory turnover, sales rates, supplier lead times, and customer demand to inform stock and order management decisions.

• Create dynamic pricing models based on supply, demand, seasonality, and campaign data.

• Deliver actionable insights that maximise revenue opportunities across multiple media channels.

• Collaborate with commercial, sales, and supply teams to embed data-driven decision-making across the business.

• Build scalable, reusable models and frameworks to inform long-term inventory and pricing strategy.

• Strong statistical modelling and predictive analytics expertise.

• Proficiency in SQL and Python (or R) for data manipulation and model development.

• Experience working with commercial, sales, or inventory datasets.

• Strong stakeholder management skills with the ability to translate complex data into clear commercial recommendations.

Register your interest by sending your CV to Daniel Abbasi via the apply link in the page.