Anaplan
Data Scientist
- Location
- London, United Kingdom
- Last checked
- Oct 26, 2026
About this role
At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market.
What unites Anaplanners across teams and geographies is our collective commitment to our customers’ success and to our Winning Culture.
Our customers rank among the who’s who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform.
Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating our wins – big and small.
Supported by operating principles of being strategy-led, values-based and disciplined in execution, you’ll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together!
About The Role
We are seeking a versatile Data Scientist to join the Functional Analytics team. The core mission of our team is to design and develop new functional analytic capabilities or “engines” that integrate directly into Anaplan Applications to solve complex enterprise business problems.
In this role, you will develop best-in-class ML models and pipelines to drive automated, actionable insights at scale. Beyond core development, you will act as a key technical partner for our customers. You will play a highly visible role in driving customer onboardings, getting hands-on with client data to customize and fine-tune our ML engines so they seamlessly adapt to complex business realities and specific customer needs.
Your Impact
- Customer Onboarding & Engine Customization: Lead the technical deployment of our engines for new clients. Partner closely with customers, Customer Success, Product, and Engineering to adapt and customize ML models, ensuring they accurately reflect client-specific business logic, operational guardrails, and unique data nuances.