Written by: Mariana Motta
Reviewed by: Cinthia Tanaka

Nubank was created with the purpose of simplifying the financial system and empowering people to make better decisions. We use technology, design, and data science to develop products and services that help our customers take back control of their financial lives.

To ensure a positive experience for all candidates, we’ve designed a recruitment process that promotes interactions with our team, allowing us to deeply understand the essential technical skills for these roles.

Data Science and Machine Learning Engineering at Nubank

Our team is composed of two distinct roles: Data Scientists and Machine Learning Engineers. Generally, Data Scientists focus on analysis and business aspects, while Machine Learning Engineers concentrate on engineering and infrastructure. However, these responsibilities can overlap, and in this article, we explain the differences between the two roles. In this video, you can learn more about the daily routines of our team members.

This team plays an essential role in every aspect of our business, from customer support to defining the credit limits we offer. We have team members performing these functions in all three countries where we operate, across every Nubank product and initiative. Our team is diverse, with members from backgrounds like physics, economics, and engineering. This diversity is crucial to building simple, fair, and truly human products. 

What our team is working on:

  • Developing the next generation of Fraud and Credit models, incorporating innovative data sources and using cutting-edge techniques;
  • Creating tests and personalization capabilities for our products;
  • Providing meaningful products using LLM to internal teams, facilitating product development, experimentation, and value proposition;
  • Democratizing AI by offering all Nubankers safe, scalable, and accurate machine learning solutions that support their work;
  • Creating AI-based decision models to guide customers through their life cycle journeys;
  • Consolidating real-time data into reusable assets that power our predictive models;
  • Monitoring the infrastructure that captures, stores, and visualizes data flowing in and out of our predictive models.

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How does the hiring process work?

Just like all initiatives at Nubank, we combine technical and cultural assessments during the recruitment process. Here’s how it works:

Interview with Talent Acquisition team

Candidates with profiles that match our positions are invited to a remote interview with the recruitment team. The goal of this stage is to:

  • Explain the Data Science and Machine Learning Engineering teams, work dynamics, and available positions;
  • Understand candidates’ backgrounds, professional experiences, technical skills, and career aspirations;
  • Evaluate how the candidate responds to certain scenarios and how they can culturally contribute to our company.

More than just an interview, this meeting is an opportunity for a transparent conversation where both parties share expectations and clarify any doubts.

Technical Stages

After passing the Talent Acquisition team interview, candidates are invited to participate in three technical interviews, each evaluating different essential dimensions for these roles.

For Data Scientist roles, the stages are:

  • Basic programming in a live coding format;
  • Modeling, to understand how the candidate structures and proposes a machine learning solution within a defined scope;
  • Business analysis, to assess how the candidate approaches a business problem and proposes a technical solution.

For Machine Learning Engineering roles, the stages are:

  • Basic programming in a live coding format;
  • Architecture, to understand how the candidate solves problems related to putting machine learning models into production within a specific context;
  • Advanced programming, to assess how the candidate applies technical knowledge to solve business problems.

Leadership Interview

At this stage, we do a final validation of values and the candidate’s seniority level within our career structure. The leadership team provides detailed insights into the challenges of the area, structure, key projects, and expectations for the role.

Decision and Offer

After gathering feedback from everyone involved in the interviews, we make a hiring decision. If the outcome is positive, we present the job offer details over a call, clarifying any questions and aligning on the start date. Once the offer is accepted, we begin the onboarding process, which includes an immersion into our culture, business, and technology.

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