Mission context
As we migrate from on-premise servers to Domino Datalab, we are looking for someone with experience in navigating constraining environments and identifying solutions with available building blocks. The challenges of the platform are combining data availability, security, and traceability in order to build the Data Science platform of tomorrow.
As a Product Owner you must take into account the requirements of your stakeholders, your clients (both data scientists, and AI consumers) as well as current and upcoming regulations in order to deliver a working AI platform for both model design and industrialization.
Domino Datalab allows our data scientist to work mostly in a self-service manner. Nevertheless, we must propose an industrialization path for their outputs with an integration into the banking ecosystem and it’s constraints (integrity, availability, throughput, …)
Another key challenge is to make data available to data scientist in a secure and compliant way.
Access should be simple, forthcoming and compliant while enabling both data exploration and use case industrialization scenarios.
As a Product Owner, you build the roadmap for the coming year making sure that the Development Team understands both the product and it’s target vision. With the Development Team’s help you make sure that work is prioritized in consistent Sprints.
Function description
Design phase and project methodology:
- build the product’s big picture including its roadmap and translate this picture towards the development team
- clarify the need and draft the product’s key functional specifications according to the agile methodology
- iteration planning and priorities definition to ensure the proper content delivery
- defines the priorities and monitor the Product Backlog, thus he continuously prioritizes the key business needs
- make sure that the different stakeholders become and stay aligned on the product to be delivered
Project follow-up and support:
- initiate a priority management approach, ensure the product consistency and quality
- actively participate into Scrum ceremonies (agile retrospectives, demo, test labs…)
- is the decision maker or has the authority to arbitrate on delivered functionalities
- conduct Sprints Backlog live adjustments and follow-up
Agile Requirements
An analyst involved in Agile projects must have the “Agile mindset” which implies:
- a positive attitude and pragmatism
- thirst for knowledge: Agile is about learning and adapting. Knowledge sharing is key to success.
- The goal of team success: Agile is about the success of the team, no individual success or heroic behavior. It is more important for the team to succeed than for the individual to have completed his/her tasks.
- There is no failure, only feedback: Agile is about taking everything as lessons, and adjusting actions based on the feedback, resulting in continuous improvement.
Beyond the roles: Agile teams are cross-functional. All required disciplines are represented in the team (analysis, development, testing, … ). However, although team members have a primary role representing a discipline, they are expected to take on other roles and contribute to other disciplines whenever it helps towards reaching the sprint goal.
Required experience / knowledge
- 5 years working with AI models and their deployment
- Knowledge of the BNP group
- Knowledge of the department’s business environment
- Expertise in agile methodologies
- Budget Management
- Project Management
Soft Skills
- Ability to see the overall picture (helicopter view) – strategic thinking
- Analytical mind – conceptual thinking
- Negotiation and persuasion skills
- Structured approach
- Quality-minded and eye for detail
- Goal-oriented
- Open to change
- Leadership