Data Scientist - Internal AI

You probably remember the best interaction you’ve ever had with a company, and the worst one too. But what makes one customer journey feel effortless, personal and relevant, while another feels slow, generic and frustrating?

With our AI-powered Customer Engagement Platform, we connect conversations, customer data, ticketing and payments through AI Agents. This allows companies and events all over the world to elevate their entire customer journey and grow faster, together.

All our services are fully integrated within our unique business structure, consisting of four business units: Connect, Pay, Live, and Engage. Our Internal AI Team drives automation to scale value across all of them.

That’s where you come in.

We’re looking for a Data Scientist to join our Internal AI Team

What you will do

We are building the ultimate Global AI-Powered Customer Engagement Platform that lets businesses connect with customers, automate work, and handle every interaction through one reliable, user-friendly platform. That is our mission for 2026.

To accelerate our impact, we’re building an Internal AI Team and are seeking a Data Scientist with a strong finance background who is passionate about applying AI to solve real-world challenges.

As a Data Scientist, you will play a pivotal role in our lean but ambitious Internal AI team. Currently, the team consists of three members: Michael (Senior Developer), Kane (Data Scientist), and Rogier (Lead Internal AI). The team is directly supported by our company founder and COO, Gilbert Gooijers. You will also collaborate closely with our Head of Business Finance, CFO, and other talented colleagues across the finance teams.

Your mission?

Your role will focus on embedding AI into end-to-end financial workflows, applying ESSA (Eliminate, Standardize, Simlify, Automate) principles and enabling collaboration across the organization. By orchestrating AI-driven processes.

Fuelled by our own Customer Data Platform, we aim to not only to enhance efficiency but also introduce the concept of Conversational Finance. This holistic approach to AI in finance will allow us to fully realize the potential of automation and intelligence, driving meaningful impact for our customers/suppliers and across the organization.

In your first 12 months, you will:

• Investigate and analyse our finance processes to gain strong business process understanding

• Elevate both customer-, supplier-, and internal stakeholder journeys in Finance by integrating Agentic AI with CM.com products

• Prioritize with your key stakeholder to identify opportunities for AI and data-driven automation

• Build, deploy, and maintain machine learning models and analytical solutions

• Start with automating workflows using Claude Cowork, N8N and of course our own Agentic platform HALO

• Work closely with the AI Lead to shape solution strategies and project priorities

• Partner with the Developer to integrate models into internal tools or production systems

• Share learnings and best practices to support AI literacy across the company

That’s why you ideally have

• A Finance background is required

• Experience in a Data Science or ML Engineer role

• Solid knowledge of Python (pandas, scikit-learn, etc.) and SQL

• Experience with deploying models into production environments

• Experience in building agentic workflows in multiple platforms

• Strong problem-solving skills and a knack for simplifying complexity

• Fluent in English and Dutch.

The reality at CM.com

We believe honesty creates better matches. So here it is:

• You work on-site with your team, collaboration happens face to face.

• Priorities can shift quickly as technology and insights evolve.

• AI will change how your role looks, fast. Some tasks you do today may disappear next year.

• We move before everything is finished or polished.

• For this position, we can only consider candidates who already hold a permanent work and residence permit for the Netherlands.

If that excites you, you’ll probably feel at home here. If you’re looking for predictability and fixed boundaries, this may not be the right place.