What is a Data Scientist at Dataiku?

At Dataiku, the Data Scientist role is uniquely positioned at the intersection of technical execution and strategic enablement. Unlike traditional roles where you might work in a silo to optimize a single model, Data Scientists here are often the champions of "Everyday AI." You are not just building models; you are demonstrating the power of the Dataiku DSS (Data Science Studio) platform, solving complex client challenges, and helping democratize data usage across organizations.

This position is critical because Dataiku’s value proposition relies on showing customers—from technical engineers to business analysts—how to leverage data effectively. You will likely work on a diverse array of use cases, ranging from predictive maintenance in manufacturing to churn prediction in retail. The role demands a high level of versatility; you must be comfortable diving deep into code and architecture one moment, and then pivoting to explain the business value of an algorithm to a C-level executive the next.

Getting Ready for Your Interviews

Preparing for an interview at Dataiku requires a shift in mindset. While your technical skills in Python, SQL, and Machine Learning are the baseline, your ability to communicate and collaborate is what will set you apart. The hiring team is looking for professionals who can bridge the gap between complex data science and tangible business outcomes.

You will be evaluated primarily on the following criteria:

• Technical Communication – This is paramount. You must be able to explain complex machine learning concepts to non-technical stakeholders without losing nuance.
• Problem Scoping & Business Sense – Interviewers assess how you approach vague problems. Can you translate a broad business pain point into a concrete technical roadmap?
• Applied Machine Learning – Beyond theory, you need to demonstrate practical knowledge of the ML lifecycle, including data cleaning, feature engineering, model selection, and deployment in production environments.
• Cultural Fit & Collaboration – Dataiku prides itself on a collaborative culture. You will be tested on your humility, your willingness to teach others, and your ability to navigate cross-functional team dynamics.

Interview Process Overview

The interview process at Dataiku is thorough and designed to test your skills in real-world scenarios. Based on recent candidate experiences, you should expect a multi-stage process that can take anywhere from 3 to 5 weeks. The company places a heavy emphasis on "fit"—both technical fit for the role and cultural fit for the organization. The process is rigorous but generally described as transparent, with interviewers who are highly motivated and engaged.

Unlike companies that rely heavily on abstract algorithmic puzzles, Dataiku’s process focuses on practical application. You will likely encounter a mix of standard screening calls, a deep-dive technical interview, and a substantial case study or presentation. A distinctive feature of their process is the use of roleplay scenarios, where you may be asked to simulate a meeting with stakeholders to scope a problem. This tests your consultative skills in real-time.

This timeline illustrates the typical progression from initial contact to the final decision. Use this to manage your energy; the Take-Home/Presentation and Roleplay stages are the most demanding and require significant preparation time. Note that the process may vary slightly depending on whether you are applying for a strictly internal R&D role or a customer-facing Data Science position.

Deep Dive into Evaluation Areas

To succeed, you must demonstrate strength across three primary pillars: Technical proficiency, Business Acumen, and Cultural Alignment.

Practical Machine Learning & Data Engineering

This area tests your hands-on ability to work with data. Interviewers are less interested in your ability to memorize formulas and more interested in how you handle data in the real world. You need to show that you understand the "plumbing" of data science, not just the modeling.

Be ready to go over:

• Data Preparation – Strategies for handling missing data, especially in production environments (e.g., imputation techniques).
• Model Selection – Justifying why you chose a specific algorithm (e.g., Random Forest vs. XGBoost) based on data size, interpretability, and latency requirements.
• Productionization – How you deploy models, monitor for drift, and handle retraining pipelines.
• Advanced concepts – Knowledge of MLOps best practices and experience with data engineering tasks (ETL pipelines) is often a differentiator.

Example questions or scenarios:

• "How do you impute missing data when you have a machine learning model already in production?"
• "Walk us through a past project where you had to build a data pipeline from scratch."
• "Explain the trade-offs between two different classification algorithms for this specific dataset."

Business Case Scoping & Roleplay

This is often the most challenging part of the Dataiku interview. You may participate in a 90-minute roleplay or a presentation where interviewers act as stakeholders (one technical, one non-technical). Your goal is not just to "solve" the math, but to define the problem, manage expectations, and propose a viable solution.

Be ready to go over:

• Requirement Gathering – Asking the right questions to clarify ambiguous business goals.
• Solution Design – Proposing a solution that balances technical complexity with business value.
• Stakeholder Management – Handling pushback from "clients" who may have unrealistic expectations or lack technical understanding.

Example questions or scenarios:

• "We are a retail client seeing a drop in sales. How would you approach this problem using our data?"
• "Present your solution to a non-technical marketing director and a technical lead simultaneously."
• "A stakeholder wants to use a Deep Learning model for a simple problem. How do you handle this conversation?"

Cultural Fit & Communication

Dataiku values low-ego, high-impact individuals. This assessment runs through every interaction but is specifically targeted in the final rounds, sometimes involving senior leadership.

Be ready to go over:

• Collaboration Style – How you work with product managers, sales teams, and other engineers.
• Learning & Adaptability – Demonstrating curiosity and how you keep up with the rapidly evolving AI landscape.
• Values Alignment – Showing a genuine interest in democratizing data and empowering others.

Key Responsibilities

As a Data Scientist at Dataiku, your day-to-day work is dynamic. You will spend a significant portion of your time building and deploying models, often using the Dataiku DSS platform itself to demonstrate best practices. You are expected to be a power user of the tool, pushing its limits and providing feedback to the product team.

Collaboration is central to the role. You will frequently partner with Sales Engineering and Customer Success teams to help scope technical solutions for prospective or current clients. This involves translating vague business desires into concrete data science projects. For internal-facing roles, you will work closely with Product and Engineering to embed AI features into the platform or analyze internal usage data to drive strategy.

Additionally, you will act as a subject matter expert. This means you may be involved in content creation, such as writing blog posts, creating tutorials, or presenting at conferences. You are not just a coder; you are an advocate for data science best practices, helping to elevate the technical maturity of the teams and clients you work with.

Role Requirements & Qualifications

Candidates who succeed at Dataiku typically possess a blend of strong technical foundations and consultative soft skills.

• Technical Skills
  • Proficiency in Python and/or R is non-negotiable.
  • Strong SQL skills for data manipulation.
  • Experience with standard ML libraries (pandas, scikit-learn, xgboost, etc.).
  • Understanding of Big Data technologies (Spark, Hadoop) and cloud platforms (AWS, GCP, Azure) is highly valued.
• Experience Level
  • Typically requires 3+ years of relevant experience in data science or analytics.
  • A background in consulting or a client-facing technical role is a significant advantage.
• Soft Skills
  • Exceptional presentation skills; ability to simplify complex topics.
  • Strong empathy for the user/client experience.
  • Ability to manage time effectively across multiple projects.
• Nice-to-Have vs. Must-Have
  • Must-have: Deep understanding of ML algorithms and the ability to explain them.
  • Nice-to-have: Prior experience specifically with Dataiku DSS or similar visual ETL/AutoML tools (Alteryx, Knime).

Common Interview Questions

The questions at Dataiku are designed to test your thought process rather than your ability to memorize syntax. Expect a mix of behavioral inquiries and technical deep dives that reflect the reality of the job.

Technical & Conceptual Machine Learning

These questions assess your theoretical understanding and your ability to apply it practically.

• "How would you explain the concept of Random Forest to a 5-year-old?"
• "What is the difference between L1 and L2 regularization, and when would you use each?"
• "How do you handle data imputation in a production pipeline when the incoming data stream is unpredictable?"
• "Describe a time you had to optimize a model's performance. what metrics did you focus on and why?"

Behavioral & Situational

These questions focus on your soft skills and how you handle workplace challenges.

• "Tell me about a time you had a conflict with a stakeholder regarding a technical decision. How did you resolve it?"
• "Describe a complex project you worked on. What was your specific contribution?"
• "How do you prioritize tasks when you have multiple deadlines approaching?"
• "Why Dataiku? What specifically about our platform or mission interests you?"

Case Study & Scoping

These are open-ended questions often used in the roleplay or presentation rounds.

• "A client in the banking sector wants to reduce fraud. Walk me through how you would scope this project from day one."
• "We have a dataset of customer transactions. Identify three potential use cases that could drive revenue."
• "Your model is performing well in testing but failing in production. How do you debug this?"

Frequently Asked Questions

Q: How technical is the interview process? The process is technically rigorous but focuses on applied data science. You won't likely face obscure LeetCode hard problems. Instead, expect to write clean, production-ready code for data manipulation and to demonstrate a deep, intuitive understanding of how algorithms work and how they are deployed.

Q: Do I need to know how to use Dataiku DSS before the interview? No, prior experience with Dataiku DSS is not a strict requirement, though it is a "nice-to-have." However, showing curiosity by researching the platform, watching tutorials, or signing up for a free trial to understand the product's philosophy will leave a very strong impression.

Q: What is the "Roleplay" interview? This is a distinctive part of the Dataiku process. You will be placed in a simulated meeting with two interviewers acting as stakeholders (one technical, one business). You are expected to lead the conversation, ask clarifying questions, scope the problem, and manage expectations. It tests your consulting and communication skills in real-time.

Q: Can I work remotely? Dataiku has a hybrid culture with major hubs in New York, Paris, and London. While some roles are advertised as remote, recent interview experiences suggest that for many positions, there is a preference for candidates who can be present in the office or are located near a hub. Be sure to clarify this with the recruiter early on.

Other General Tips

• Know the Product: Even if you haven't used it, read about Dataiku DSS. Understand its "visual recipe" approach versus coding, and be ready to discuss why a platform like this is valuable to enterprises.
• Focus on the "Why": In technical questions, don't just give the answer. Explain why you chose that approach. Dataiku values the thought process and the ability to justify decisions over getting the "right" answer immediately.
• Prepare for the Presentation: If you are given a take-home case, treat the presentation deck as a professional deliverable. Ensure it is visually clear, tells a compelling story, and addresses both business value and technical feasibility.
• Be Consultative: During the roleplay or case study, do not rush to a solution. Ask questions. Challenge assumptions politely. Act like a partner trying to solve a problem, not a test-taker trying to pass an exam.

Summary & Next Steps

The Data Scientist role at Dataiku offers a unique opportunity to work at the forefront of Enterprise AI. You will be challenged to not only be a great technical practitioner but also a strategic advisor who can bridge the gap between data and business value. The interview process is designed to find this specific blend of skills, focusing heavily on communication, practical application, and cultural fit.

To succeed, focus your preparation on explaining complex concepts simply, scoping ambiguous business problems, and demonstrating a collaborative spirit. Review your past projects in depth, ensuring you can articulate the business impact of your technical choices. With thorough preparation and a consultative mindset, you can navigate this process with confidence.

The salary data above provides a baseline, but compensation at Dataiku can vary significantly based on location (e.g., Paris vs. New York vs. London) and seniority. Be prepared to discuss your expectations with the recruiter early in the process, as the package typically includes a mix of base salary, bonus, and equity.

Good luck! You have the roadmap—now go show them why you are the right fit for Dataiku.