Dough Life: A Day in the Life of a Loblaws AI Analyst

Name: Tina Yao

Role: Artificial Intelligence / Machine Learning Analyst at Loblaws

About Me

Hello! My name is Tina Yao and I am currently an Artificial Intelligence / Machine Learning Intern at Loblaws Companies Ltd. I am also currently studying in the Masters of Management in Artificial Intelligence (MMAI) program at the Schulich School of Business, graduating in August 2020.

This Loblaws role is an 8-month internship. Essentially, I use machine learning to solve problems efficiently, such as solving product and pricing issues or enhancing operational flows.

In my free time, I run a life-style blog called “Eunoia Inspired” - check it out by clicking here!

1. What is your role? What does your job entail?

Hi, I’m Tina Yao! I am currently interning at Loblaws Corporation, as a Machine Learning/AI Analyst (Artificial Intelligence). This is part of their AI/ML Center of Excellence, or part of their Data Analytics branch.

My role is essentially using machine learning to solve problems efficiently, so I work with different teams and departments. The work that we do makes a huge impact on many departments and can be used to match products and prices. It has a number of additional functionalities but that’s the main goal. For example, some additional uses for it could be data governance. Externally, it could compare competitor prices.

2. What does a typical day look like for you? How is your work-life balance?

Since it’s an internship, it’s not the typical 9-to-5 role as it’s often more part-time. The entire team at Loblaws (in the Center of Excellence) already works remotely. One aspect I enjoy: It doesn’t matter how long you take, as long as you finish your work.

We used to meet up twice a week, but due to COVID, we meet online now just once a week to update each other on accomplished tasks. Sometimes my team will meet internally without our project lead to figure out issues or resolve coding bugs. After each meeting, we figure out the tasks going forward and then work on it individually.

My workday doesn’t have a “set objective". In a sense, it's more exploratory. For example, in software development, we first build a proof of concept before building the models. It’s actually really fun ... coding can get frustrating, but it’s definitely very rewarding when the code works and there are practical results.

As for my work-life balance, I have a lot of other things going on such as running my blog Eunoia Inspired (check it out here!), but it’s very manageable since it’s part-time and flexible in nature.

3. What appealed to you about this particular organization?

Remote Work:

The entire ML/AI division works remotely and fortunately, I had the option to work from home before COVID-19 began. People love going to work since there are many cross-team meetings and the building itself is just amazing. For example, we needed another team’s input on one aspect of our project so it was easier to meet in person. In general, people tend to go to the head office for collaborative work. There is the flexibility to work from home, and Loblaws also rents places across the GTA (such as in Downtown Toronto) where you can work if you’re a Loblaws employee.

We currently use Microsoft Teams to hold our meetings. With the changing environment, we have to conduct work virtually and there can be issues with Internet connection. Personally, I would prefer in-person meetings. You can meet up anywhere and you don’t need to meet at the head office. For example, the team lead works downtown, and since most of us are closer to downtown anyway, we’d pick a café for collaborative work. My team is incredibly kind, and it’s also a great bonding experience to get to know each other better (e.g. having meals together), and share favourite restaurants! I love my team!

Physical Location:

The Loblaws building in Brampton is beautiful! It uses an open-office concept so there’s plenty of natural light. It’s absolutely modern and amazing. There is no fixed office or desk, so the space is first-come-first-serve!

Additionally, it's a comfortable place to allow for many collaborative opportunities. For example, there’s a whiteboard to collaborate on and write your ideas down. I also love the natural lighting in the office, as there are glass windows with lots of sunlight and a great view. My favourite memory is the rainbow coloured booths, where you can hook up your laptop for work.

4. What aspects of your job do you enjoy? What keeps you up at night?

Culture:

My team is very smart and I’m fortunate to have them support me. I’m surrounded by intelligent people who love to support this project, especially in learning and following new technology trends.

They are incredibly positive and patient, and although the Schulich MMAI program mandates a certain scope for this role, my team is willing to expand the learning opportunities to help me. I love that they are all super supportive, especially as I will be looking for full-time employment soon. They are supportive in encouraging me to present and work on this project, as well as expand my connections at Loblaws. Even with different roles and hierarchies, I have met senior management and different team members who connect with me on a personal level and are very friendly.

The Work:

The work is not mundane, administrative work. It's great since I love to be creative and do different things. You are not in the loop doing repetitive work! With Machine Learning and AI, the nature of the work involves problem-solving. Most importantly, the ML/AI team encourages lots of trial and experimentation, which is what I love about the work and culture.

Cafeteria:

The Loblaws building has a cafeteria and the food is amazing! Loblaws has a full cafeteria with different food options every day! You have chefs who make food for you on the spot with endless options such as a salad bar, soup and bread bar, and freshly cooked food bar (ranging from Udon Ramen to Mexican food!). I also heard that they switch up the menu DAILY so you can eat something different 365 days of the year!

RECRUITMENT PROCESS

5. What would the ideal candidate look like? Describe any relevant skills, experiences, traits, etc.

I would break it down using hard and soft skills:

Hard Skills:

You should have a strong technical background (i.e know programming) and have a very high level knowledge about Machine Learning and Data Analytics. Some examples would include a knowledge of SQL and Python coding. To be honest, any coding language would be useful. Specifically to machine learning, a knowledge of KERAS and Tensorflow can be helpful. Finally, most teams under the “Data Stream” work with time-series data. If you have experience in time-series prediction, that would help as well.

Soft Skills:

Communication, teamwork, problem solving and creativity. For example, there will be times where after having done your data analytics or modelling, you will need to present the results and implications to people who don’t necessarily have a strong technical background. You will need strong soft skills to explain the implications of your work!

6. What did the application/interview process look like for you? How did you get your foot in the door?

In the first stage, I sent an application (resume and cover letter) for this role. However, the recruitment process may look different for a full-time role since my experience is based on the 8-month internship.

This role was linked to a course in the Schulich MMAI program, and is considered a “Capstone Project”. All the students are required to send in their resumes, which the companies can pick from. The recruitment is facilitated by the Schulich MMAI program. After a few more stages of screening, the company paired me to the Loblaws team and I was introduced to the department.

Getting through the door:

Due to the structure of the MMAI Program, the process of getting the internship was primarily through one's resume and cover letter.

While your resume may not the most important thing for this program, a good resume is always beneficial. I had multiple versions which were edited by multiple people, with a focus on three main things: who I am, what I’ve done, and my technical skills. My “About Me” included information such as “Recent graduate, masters program, and hardworking individual”. My experience section included my previous internship at Bell Canada, and focused on emphasizing my projects and experiences with Machine Learning and other Technical skills. It’s always recommended to put past technical (or relevant) projects on your resume - even if they were school projects, especially for technical roles. With each experience, I was brief and focused on writing about the skills that they looked for. Finally, the skills section included both technical and soft skills for me.

I also included a section on extracurriculars on my resume, such as my blog, Eunoia Inspired, my mini-startup idea, club involvement, and more. It’s important to include this if you have room to show a different side of yourself and highlights your soft skills like leadership. While my experiences are data-driven, I wanted to show the creative side of me through these extra-curriculars.

7. How did you stand out in the application process?

Technical is king! If you have valuable experiences that are relevant, include it in your portfolio and your resume. If you have a Github, include the link for that. You should also read the job descriptions carefully, apply to roles that interest you, and leverage any connections you have. There are additional things you should do to show your expertise.

In the AI Space, you should know the most frequently asked AI questions in interviews, do your research and create your own responses to these questions. After all, you want that process to be conversational.

8. Looking back, was there anything you could have improved during your application process, or mistakes that you noticed other applicants had made?

My biggest area of improvement would be to build up my project portfolio. There will be jobs that ask for more work, and you should have something available to show.

One common mistake I saw was time management. For my colleagues, you have to make sure that you’re being productive and be able to balance your time with academia, projects and job applications. Since AI and Machine Learning is such an exploratory field, a lot of things require research and most of coding is self-taught. For example, my peers regularly gave me resources to learn on my own. In short, do research about your field and what you need to do to stand out. In AI, taking initiative, teaching yourself and starting your own projects is king!

If you have strong connections with your professors or teaching assistants, you can leverage them to prepare for future interviews or potentially get new connections! I formed great connections in the MMAI Program and I know that when I get a job or encounter a coding issue, I can reach out to my contacts.

9. Were there any surprises in your job search experience or things you wished you had known earlier?

Great question! I didn’t receive any surprises during my job search process. The only thing I wish I knew was how many resources were available. There are great databases for researching how to break in or stand out in AI roles, so you should check it out. A quick google search can help you! You should also be aware of the sensitivity of the system in terms of looking at keywords, formatting, and how you need to find ways to stand out.

10. What advice would you give to someone who wants to break into your role/program?

1. Get your technical skills up! This field is very much dependent on technical skills. (Soft skills are also important.)

1. Projects are 10% coding and 90% convincing your stakeholders about the project.

2. Strong soft skills (i.e. good communication) are needed to create presentations, but the focus should be technical skills.

3. Be able to communicate technical skills to people who aren't necessarily strong in areas like programming.

2. Do projects on the side! (ex. Kaggle competitions.) Organizations will notice you if you've done well in a competition. This is important if you don’t have enough job experience that directly relates to the role.

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