Introduction
- 36 hours per week
- Start date: ASAP
- End date: 01 May 2027 with the possibility of extension.
- Hybrid way of work.
- ZZP is not possible.
- Only residents of the Netherlands can apply. No relocation is possible.
Organization/ Department
The team originally consisted of three data scientists, with a focus on ensuring that each data scientist is supported by an engineer who can productionize their use cases. These use cases are concentrated within defined risk areas.
As part of the ongoing scale-up, additional resources have been added. Several existing data engineers have also rotated into these roles to strengthen the team's delivery capabilities.
The broader data science capability now includes approximately ten data scientists, with five operating within the core data domain. In addition, stakeholder groups such as Accounting have their own data science resources that collaborate closely with the team.
The overall organization is continuing to grow. The primary area of expansion is engineering, with hiring focused on reaching the target team size needed to support the increasing volume of data science use cases and their productionization.
Job description
We require this extra capacity to fulfill the usecases backlog for COMPI regnexus. This person will work on the genAI usecases funnel, assisting and advising on business proposals that are in the early stage, and working on design and implementation in later stages, and finally assisting with operational handovers.
AI is being leveraged to support various stages of business processes across a broad stakeholder landscape, including Accounting, Controlling, Asset and Liability Management (ALM), Strategic Investment Management (SIM), Non-Financial Risk, and Financial Risk.
The organization is currently scaling its AI capabilities and is looking to build a balanced mix of internal and external talent to support this growth. There is an active backlog of approximately 28 AI-related use cases. Several use cases have already been moved into production, while others are technically ready but are still awaiting business approval before full deployment.
Both the AI function and its governance framework are still in the early stages of maturity, with processes and structures continuing to evolve as the capability expands.
The organization operates with a chapter-based structure and has a number of established chapter leads who provide leadership and oversight across the different domains.
The scope of work varies significantly across initiatives. In some cases, the focus is on building robust and scalable data pipelines that can support multiple future use cases. In others, the work is centered on developing and deploying machine learning solutions to address specific business challenges.
With the following results (SMART)
There are 5 usecases currently in the funnel for 2026. The aim is to complete these before year end.
Requirements
-8+ years of experience as a Machine Learning Engineer, or a hybrid ML/Data
-Strong focus on improving data quality and enabling advanced analytics and AI use cases
-Ability to lead technical initiatives, guide the team, and influence architectural and engineering decisions
-Strong software engineering mindset with emphasis on:Clean code
-Maintainability
-Scalable design patterns
-Software development best practices
Hands-on expertise with:
Python
Spark
PySpark
Databricks
Azure
SQL
AI/ML services
-Experience building production-grade ML and data solutions rather than purely data science-focused work
-Able to define engineering standards, establish best practices, and mentor team members
-Strong communication and stakeholder management skills
-Understanding of machine learning concepts and model deployment in enterprise environments
-Experience in the financial services domain is a strong plus
-Knowledge of customer analytics, risk, or finance-related use cases is beneficial
