1. What is a Project Manager at Dataiku?

At Dataiku, a Project Manager plays a pivotal role in orchestrating the deployment, adoption, and success of our Everyday AI platform. Unlike generic project management roles, this position sits at the intersection of advanced technology, business strategy, and organizational change. You are not just tracking timelines; you are enabling large organizations to operationalize machine learning and data science at scale.

This role requires navigating complex stakeholder landscapes, often bridging the gap between technical teams (data scientists, engineers, architects) and business leaders. Whether you are focused on Professional Services (guiding customers through implementation), Internal Operations, or specific thematic areas like Corporate Social Responsibility (CSR), your work directly impacts how effectively users can leverage data to drive value. You ensure that ambitious AI initiatives are delivered on time, within scope, and with high adoption rates.

You will likely be working with the Data Science Studio (DSS) platform, helping teams move from raw data to deployed models. The environment is fast-paced and collaborative, requiring you to be a proactive problem solver who can thrive in a hybrid culture that values autonomy and impact.

2. Common Interview Questions

3. Getting Ready for Your Interviews

Preparing for the Dataiku interview process requires a shift in mindset. You are not just being evaluated on your ability to make a Gantt chart; you are being tested on your ability to manage ambiguity, influence without authority, and understand the nuances of the data lifecycle.

Key evaluation criteria for this role include:

Project Governance & Methodology You must demonstrate a robust command of project management frameworks (Agile, Scrum, Waterfall) and how to adapt them to data projects. Interviewers will evaluate how you structure complex initiatives, manage risks, and handle scope creep in environments where requirements often evolve.

Stakeholder Management & Communication Dataiku operates in a high-touch environment. You will be evaluated on your ability to communicate complex technical concepts to non-technical stakeholders and vice versa. Expect to discuss how you handle difficult client situations, align conflicting priorities, and keep "aloof" or busy stakeholders engaged.

Domain Knowledge & Technical Aptitude While you do not need to be a coder, you must possess strong data literacy. Depending on the specific team (e.g., CSR, Implementation, R&D), you may face questions regarding data privacy, AI ethics, or sustainability reporting. You need to show that you understand the product you are managing.

Adaptability & Resilience The process at Dataiku can be rigorous and priorities may shift. Interviewers look for candidates who remain composed under pressure, can pivot quickly when business needs change, and maintain a positive, solution-oriented attitude during lengthy engagement cycles.

4. Interview Process Overview

The interview process for the Project Manager role at Dataiku is comprehensive and rigorous. Based on candidate data, you should expect a multi-stage process that can span 5 to 6 rounds over the course of 4 to 6 weeks. The company takes hiring seriously and aims to ensure a strong fit for both technical aptitude and cultural alignment.

You will typically begin with a screening call with a Talent Acquisition Manager to discuss your background and interest. If successful, you will move on to interviews with the Hiring Manager and potential peers. A defining feature of this process is the Case Study or Presentation round, which often requires significant preparation time (3+ hours). This stage is critical; it is where you demonstrate your practical skills in a simulated environment. Be prepared for a process that tests your endurance and commitment.

The timeline above illustrates a funnel that narrows significantly after the Hiring Manager screen. Use the time between the initial screens and the presentation round to research Dataiku’s product suite deeply. The final stages often involve meeting cross-functional partners or leadership to validate your strategic thinking and culture fit.

5. Deep Dive into Evaluation Areas

To succeed, you must prepare for specific evaluation themes that Dataiku prioritizes. Based on recent interview data, the following areas are critical:

Project Management Scenarios & Execution

This is the core of the interview. You need to show how you take a project from ambiguity to delivery. Interviewers will dig into your past experiences to see if you own your projects or just spectate.

Be ready to go over:

• Risk Management – How you identify roadblocks before they become critical issues.
• Resource Allocation – Managing constraints when teams are stretched thin.
• Methodology Selection – Why you chose Agile vs. Waterfall for a specific data project.

Example questions or scenarios:

• "Describe a project that was falling behind schedule. How did you identify the root cause and what steps did you take to recover?"
• "How do you handle scope creep when a client adds requirements mid-sprint?"

The Case Study Presentation

This is often the "make or break" round. You may be asked to prepare a presentation on a specific topic, such as a project launch plan or a strategic initiative.

Be ready to go over:

• Structure and Clarity – Your ability to synthesize information into a cohesive narrative.
• Strategic Thinking – Linking project tactics to broader business goals.
• Q&A Handling – Defending your choices during the presentation.

Example questions or scenarios:

• "Present a rollout plan for a new internal tool, including communication strategy and risk mitigation."
• "Analyze this hypothetical client scenario and propose a project roadmap."

Domain-Specific Knowledge (CSR / Data / AI)

Depending on the specific PM opening, you may face technical questions related to the domain. For example, recent candidates for specific PM tracks reported questions on Corporate Social Responsibility (CSR) and sustainability.

Be ready to go over:

• Data Lifecycle – Understanding how data moves from ingestion to visualization.
• Specialized Topics – Concepts like ESG (Environmental, Social, and Governance) reporting or AI Governance if relevant to the specific job description.

Example questions or scenarios:

• "How would you approach a project focused on improving our corporate social responsibility reporting?"
• "What are the key challenges in managing a data science project compared to software engineering?"