1. Tell us a bit about yourself, what’s your background and what drew you to Data Science?

My name is Altieris Marcelino Peixoto. I was born in Guaxupé, a small town in southwest Minas Gerais, Brazil, where my parents worked as coffee farmers. For much of my childhood, I helped on the farm, but I was always drawn to studying, problem-solving, and understanding how things worked. I knew early on that my path would take me beyond the family farm.

At 15, I used a computer for the first time, and by 18 I had decided to pursue that spark of curiosity, enrolling in a Computer Science program in a nearby town. After graduating in 2011, I moved 700 km away to Curitiba, where I began my professional journey. Over the years, I worked in software development, database administration, and business intelligence, always driven by a passion for data and how it can be used to solve real problems.

In 2014, my life changed completely. On June 16th, during the World Cup in Brazil, I was leaving a shopping mall when I was shot. I spent 10 days in a coma, 30 days in the ICU, and endured multiple surgeries. Recovery took over a year, both physically and mentally. During that time, I returned to my parents’ farm, where they cared for me. To keep my mind engaged, I turned to online courses in big data, machine learning, and data science through Coursera and edX. It was then that I discovered tools like Weka and R, innovations from New Zealand researchers that would unexpectedly shape the direction of my life.

By 2016, fully recovered, I returned to Curitiba to pursue my goal of becoming a data scientist. I worked across industries, building experience while completing a master’s degree in Electrical Engineering, where my research focused on Artificial Intelligence applied to Smart Cities.

In 2022, I was offered the opportunity to move to New Zealand, working in Data Science and AI for a company developing analytical solutions for media and entertainment. Then, on June 16, 2024, exactly 10 years after the day my life nearly ended, I joined Auror. For me, it was more than just a career move: it was a chance to apply my skills and experience toward a purpose that deeply resonates with me, helping reduce retail crime and preventing others from experiencing tragedies like mine.

2. What inspired you to join Auror, and what has your journey here looked like so far?

My journey at Auror has been filled with challenges and continuous learning. We’re encouraged to innovate, experiment, and push boundaries, all while maintaining a strong commitment to information security and the responsible, ethical use of Artificial Intelligence.

Auror offers a truly unique environment, practically a data scientist’s playground, where we tackle a wide range of problems across diverse data types, from complex networks and structured data to audio, images, video, and text.

The environment is dynamic, supportive, and highly collaborative. We value open communication, give each other space to be autonomous, and the team is growing, which makes it a really energizing place to be part of.

3. Can you share an example of a recent challenge the team solved and why it was meaningful?

A recent challenge we faced was increasing the engagement of store security guards in reporting incidents. Typically, after an incident in a store, the security guard uses our platform to record the incident, including all the details of the person of interest, products, images, and CCTV footage. However, not all events were recorded due to the need to log in to a computer to fill out the form.

Our idea was simple: how about the security guard recording a draft of the event using his smartphone at the exact moment it happened through audio and then enriching the event with more information and evidence at another time?

From there, we developed the Voice Event Report, a practical solution that automates the process of creating crime event reports through audio in seconds with the purpose of increasing the engagement of security guards in event reporting, the detail and quality of the data entered.

The app is a success and is being used in several retail chains around the world.

4. How does the team collaborate, both within Data Science and cross-functionally?

‍The Data Science team is part of the wider Product Squad that operates in streams. Each stream is made up of Product Managers, Engineers, Quality Engineers and Product Design. Auror’s culture is highly collaborative and we are often partnered with the stream as well as Customer Success teams to ensure that what we’re building aligns to the product roadmap and the wider organisational goals. Beyond developing models, our team also supports the wider business by providing guidance on how to best leverage data science, helping teams translate insights into meaningful outcomes.

5. What’s your leadership philosophy?

For me, leadership is about creating an environment where my team can do their best work. We’re a diverse group, coming from different backgrounds, walks of life, genders, and communities and as a leader, I see it as my role to make sure everyone feels valued, heard, and supported.

Leadership is not about ego, but about achieving meaningful results together as one team with a shared purpose. It’s about encouraging experimentation, learning consciously, and continuously improving. Most importantly, it’s about nurturing and protecting the unique culture that Auror has built for its people.

6. What makes now a great time to join the team?

Auror has a renewed mission, and this is to reduce 50% of violent retail theft in 5 years. Over the last few years of scale, as a Data team, we've worked hard on building foundational capabilities for building our Machine Learning models. We’re continuously iterating on these models to enable Auror to scale successfully across the world. Not only that, we’re working with really interesting multi-faceted data (eg. images, text, videos etc.) and these all pose a challenge that is really interesting to solve for, particularly building a solution with privacy at the top of mind. So, if you are someone who is up for a challenge, who wants to be part of using AI for good, this is a great time to join Auror.