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Callaghan Innovation supports hi-tech New Zealand businesses, enabling them to bring smart, enthusiastic and fresh thinking into their R&D activities. 

Via the Callaghan Innovation R&D grant funding, we were able to once again, invest in a Data Science intern on a ten-week project basis. A recent change in their criteria offered us the opportunity to bring on our first Ph.D. candidate for a paid internship, giving us additional resources to work on more innovative ideas. 

Movio is, at its core, a data company, and we’ve invested heavily in Data Science over the last few years in order to maximize the value we can extract from this data for both our clients and ourselves. As such, it made sense to invest our time and resources into the Data Science community and give a student the opportunity to put their theory to practice.

We spoke with Daniel Norouzifard (DN), currently a Ph.D. student of Computer Sciences at AUT School of Engineering, on his experience here. We also looked back on the success of last year’s intern, Het Patel (HP).

Which R&D project did you have the opportunity to work on? 

DN: I researched and developed models for predicting moviegoer behavior by employing multiple deep learning strategies.

As a Data Scientist, I planned to bring my talent and knowledge together to complete the CLV project at Movio. With the support of the global Data Science team, we discussed how best to approach the project. I was really excited to use my science and expertise to assist the company going forward and I understood that I had to deliver results in ten weeks. 

Which AI models did you work with throughout your internship? 

DN: I worked with different deep learning models to predict the Customer Lifetime Value (CLV) as a multi-time series. I completed the CLV project using deep learning models, such as Long Short Term Memory (LSTM) and Convolutional Neural Networks as well as more traditional machine learning models such as XGBoost and AdaBoost. I had an opportunity on my last day to present and share the CLV project findings with the wider Movio Crew. 

I also learned more about machine learning with Spark, to cope with big data processing and Python programming with Amazon Sagemaker.


What were some of the most important things you learned here during your internship?

DN: From my point of view, “the best way is the shortest way”, although it is not always the easiest way! I made friends and personal connections during my time at Movio, as well as learning how to network with colleagues professionally based in a New Zealand working culture. 

Unlike other corporate internships where interns tend to pick up a corporate culture, working for a medium-size company and interacting with its entrepreneurs and other people in the tech start-up ecosystems was very inspiring. My goals were to be able to connect and communicate very clearly with peers without any corporate jargon, and to be able to quickly grasp topics or ideas that are somewhat more technical, while not losing track of the big picture.

What did you enjoy most about working at Movio?

DN: This was a chance for me to identify with a New Zealand work culture (outside of my home country, Iran) that reflects my values and to help me figure out what kind of career I want as a Data Scientist. I am now more familiar with a New Zealand working environment that’s growing, innovating, and trying new things. 

Movio is one of the most progressive work environments I have worked in. Workplaces are different today from what they once were, instead of the traditional 9-5, Movio allows a flexible culture and emphasizes the importance of a healthy work-life balance.

Movio is a friendly, hi-tech company where employees engage with one another in a positive way. Employees are encouraged to explore the full potential of their skill sets, and they’re likely to grow through their job experiences. Movio is inspiring, and employees feel motivated. 

Het Patel is currently in his fourth year of Engineering Science and Commerce at The University of Auckland and interned at Movio in 2019

What R&D project did you have the opportunity to work on? 

HP: I explored the use of a variety of different deep learning architectures in order to learn embeddings for building movie recommendation systems. These embeddings could be used to improve member/movie segmentation in the cinema marketing industry. 

What did you enjoy the most about your time at Movio?

HP: My time at Movio was nothing but the best experience an intern could have. I was supervised by two great mentors, who were always ready to help and so was everyone else. It was like being part of a family.

Movio's culture was really welcoming and helped me to understand how to work in a professional environment with a great level of freedom. I worked on a machine learning project which was shadowing the work of my mentors and was a great learning experience. I learned how to research and apply the things I learned straight away. Overall, it was a good start to my career and I would recommend it to anyone.

Keep an eye on our careers page for Data Science opportunities and follow our life here at Movio on Instagram.

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