Iterating Into Artificial Intelligence: Sid’s Path from HR to Data Science & AI

Siddharth “Sid” Bhatia has a story filled with determination and change.  

He currently works as a Business Reporting Analyst at Oranga Tamariki, having transitioned into this new field from Human Resources through the Institute of Data’s Data Science & AI Programme. 

Originally from Delhi and initially trained in hotel management, Sid’s career path led him through significant positions at multinational companies before he discovered his passion for technology. 

Pursuing a formal business qualification in New Zealand paved the way for his move into the tech sector. 

By joining the Institute of Data’s Data Science & AI Programme, Sid refined his skills in Python and artificial intelligence. 

Sid shares his unique journey, the challenges he encountered, and the successes he experienced in his new career.

1. Hi Sid, please tell me a bit about yourself.

I’m originally from India. I was born in Delhi, the capital. After finishing school, I spent a significant amount of time in Bengaluru, which is known as India’s Silicon Valley.

Outside of work, I run a lot and do some light yoga. I also read a lot about upcoming technologies, especially in artificial intelligence (AI).

I also enjoy traveling. I have decided to visit at least two countries every year. Last year, I went to Singapore and Taiwan, and next month, I’m going to Korea and Vietnam. My plan is to continue doing this. I’ll cover the Southeast Asian countries first, then slowly move to Europe and the West.

2. Could you tell me about your career before you transitioned into data science & AI?

I studied hotel management in Bengaluru before starting my career as a Human Resources (HR) professional with Accenture, a multinational company. My career at Accenture took off when I transitioned into HR as an analyst, where I gained a deep understanding of employment regulations and laws. I thoroughly enjoyed my time there.

After working at Accenture, I moved to another multinational company, Capgemini, where I continued my HR career for three years. Following this, I joined a smaller semiconductor startup in Bengaluru as an HR manager.

Despite my experience in HR, I felt the need for a formal business qualification to progress further in my career. I researched my options and decided to study abroad.

New Zealand stood out for its reputable institutions, so I got myself a student visa and moved there in 2018 to pursue an applied management course at the Eastern Institute of Technology in Hawke’s Bay.

After completing the course in a year, I relocated to Wellington and started my first job at the Ministry of Business, Innovation, and Employment (MBIE). 

3. How did you decide you wanted to change careers in Data Science & AI?

When I worked at MBIE, I was a senior business administrator, handling two main roles. The first was reporting, and the second was working on an information and communications technology (ICT) project to launch a new website for a specific team. As time went on, I mostly dedicated myself to the ICT project. 

When some of the other team members working on this project left, I became the subject matter expert as I was the only one who had been involved from start to finish.

I was asked to lead the project because no one else knew what was happening. 

I recall helping a programmer and thinking to myself, “I can do this too; it’s not that difficult.” If I learned to code, I could manage the whole process. That’s how I started to get more and more interested in technology.

I started researching and found that the skills most in demand in the tech sector were cyber security, software engineering, and artificial intelligence (AI), with AI being the field on the rise.

4. Did you have any concerns about the programme before you started?

I didn’t have any reservations initially, but after the first month, I did wonder how the whole programme would unfold. 

When I was doing my research, I found that many AI courses, like the one at Victoria University, were almost four years long. Other courses were also lengthy—one or two years. So, I was confused about how everything could be covered in just six months.

5. What were the challenges and highlights of the programme?

The biggest challenges for me were the self-study part and understanding the mathematical equations behind the models we were learning.

Sometimes, there were hard-to-grasp concepts that I struggled to understand the in-depth details of, and I felt a bit lost. 

But the tutors were good, and we had really good support from the advisors – Isabella and Sakshi were very helpful and understood the pressure we were under.

They would ask if we needed more time or if we needed further explanation on anything. I usually went to Isabella with my questions, and she was really helpful.

To learn more about the concepts behind the models we were learning about, I decided to self-study probability, statistics, and calculus. I found that having the right book to support my learning was crucial. After doing some research, I found a great book, Calculus For Dummies, which was easy to understand. I’d recommend it to anyone looking to learn calculus or the programme.

Overall, the programme was busy, but it was just the right amount of busy that you should be during the week if you’re working at the same time (which I was).

6. Tell us about your capstone project.

The Capstone project was the best part of the programme.

In the programme, we learned about computer vision, natural language processing (NLP), regression, and classification models, and I kept wondering which one would be more popular and useful for me professionally.

As ChatGPT was really popular at that time, I decided to focus on NLP for my capstone project and built a recipe generator for it.

Essentially, you input the ingredients you have in your kitchen, and it generates a recipe for you.

My model allows you to input any ingredients you have, which is not the case for most other generators of this kind. I’m proud of it. 

Because of this positive experience, I’ve been building models even after finishing the programme and posting them to the website Huggingface.co (it’s named after the hugging face emoji – 🤗). I love building models and spending time on weekends practising my skills.

7. Do you have any advice for students on how to navigate the programme successfully?

The best advice I can give is to know you’ll have to focus on self-study—there’s a lot of it, and it’s important.

This is only a six-month part-time programme, but it’s similar to university courses with many assignments and self-study. That’s where you’ll learn the most. Also, if possible, learn as much Python programming as you can before starting the course.

Another tip I learned over the last 18 months: During my time at Victoria University, ChatGPT hadn’t come out yet, so I relied on Google and extensive reading to find solutions. This approach taught me more than using ChatGPT seemingly now has the ability to. What I mean by that is that while ChatGPT is useful for quick answers, Google provides more in-depth information if you really want to learn.

For example, in a group discussion, we had to clean data with many blanks. Our tutor suggested averaging them out, but I found an AI model through Google that better predicts and fills the gaps. ChatGPT didn’t offer that solution, highlighting the value of thorough research.

8. That’s unique advice. Thanks for sharing. Tell me how you landed a job in the industry.

I was looking for data analyst jobs because I thought I might not yet be qualified for data scientist or machine learning engineer roles. I found a job listing at Oranga Tamariki, the Ministry for Children here in New Zealand, that wasn’t focused on SQL or Python but on an older platform called SAS, which is still widely used. 

They were looking for someone with Python and SQL skills to help transition from SAS to cloud-based technology.

I applied, and even though I didn’t yet have experience as a data analyst, I think they appreciated my motivation because they invited me for an interview.

I shared my journey, explaining how I started and that I was still learning. They liked me and didn’t ask many technical questions but did want to know how I’d rate my skills in Python and SQL.

I was honest and rated myself a five or six out of ten, which they appreciated, saying it was good enough for them.

I am now a Business Reporting Analyst, and I’m really happy to have this role. 

9. What does your day-to-day look like at your new job?

Every day, we receive requests under the Official Information Act (OIA). These include a mix of OIA requests, parliamentary questions, media requests, and staff requests that we must respond to within 10 to 15 days. When a request comes in, it is assigned to someone on our team. We then figure out how to gather the needed data, using the SAS platform for this task. On SAS, we use a limited version of SQL and some SAS-specific programming.

Although I know a bit of SAS, I use SQL more often.

I code on SAS to pull data from our data warehouse and provide the answers required in the request.

I also spend a lot of time cleaning the data. 

10. How does it feel to have made it so far in your journey towards working in the AI industry?

It’s been incredible seeing my dreams become a reality.

My confidence levels were initially low because I wasn’t sure when I would find a job in the industry. 

However, I was determined to change industries because I couldn’t see any future in the roles I had previously. Landing this role was really a dream come true.

I am just going to keep working towards my goals.

11. What advice do you have for Kiwis considering a future in the Data Science & AI industries?

I like to consider why I want to pursue this path and use that ‘why’ to motivate me to learn.

I build models every month or two, tackling real-world problems and creating models around them. This practice not only helps solve issues but also provides solid evidence of my skills, which can be impressive during job interviews.

I also watch a lot of AI-themed movies to stay inspired — there are many out there. I’ve seen about 10 popular Hollywood films with intriguing AI concepts. One movie I enjoyed recently is called The Artifice Girl. 

You can find these movies on Google, often available on Netflix or for rent/purchase on YouTube. They offer fascinating insights into AI and its applications.

Remember, AI knowledge isn’t limited to one field; it can be applied to various areas.

I think it’s good advice to be prepared to put in the work to get to where you want to go. 

If you’d like to learn more about our Data Science & AI Programme, please download a course outline.

Alternatively, you can speak about the programme directly with a team member by booking a career consultation to start your journey with an actionable plan.

You can connect with Sid and follow his professional journey on Linkedin.