Name – Parul Pandey

Designation, company : Data Evangelist at H2O.ai

Can you please take us through your AI journey?

I graduated as an Electrical & Electronics Engineer from NIT Hamirpur,India. Post her graduation I worked as an Analyst at Tata Power, where I worked on unveiling insights into the power distribution network in Delhi. Currently I work as a Data Science evangelist at H2O.ai and I am also a Kaggle Grandmaster in the notebooks category.

What are the major challenges you faced as a woman in reaching where you are right now?

I have never felt that being a woman is a challenge since we as females bring a lot of value to the work and society as a whole. However, our society and the way our working environment work tend to take it as a negative factor at times. This happens more predominantly when women go on a career break due to maternity(or for any other reason).Becoming a mother is a personal choice, and when a female decides to become a mother, she shouldn’t be made to choose between a career or a child. Unfortunately, this does happen all too often. I’ve seen women are denied promotions or projects because they would be going on maternity leave and I have faced that too. Exceptions do exist, but this is still a fairly common phenomenon.

What made you interested in AI?

I have always been fascinated with numbers, but my real tryst with Data Science occurred when I joined my first job. I had been inducted into a department that was responsible for the analysis and planning of the Power Distribution network . There I learnt how to crunch numbers, analyse them to get insights, and perform predictive analysis. This got me hooked into nuances of data science and machine learning. However, all these analyses were being done using proprietary tools. I would sometimes wonder if the processes could be replicated using open source tools and the current best practices in data analysis, predictive analysis, etc. Sadly, due to a hectic schedule, I couldn’t give much time to these thoughts, but then I went for my maternity leave which gave me a chance to reflect on my life and career, and if I was actually enjoying what I was doing. In a way, I reinvented my whole career during my maternity leave!

What's your area of expertise in AI and why chose that one?

Currently, I work as a Data Science evangelist, where my job is to interact with the community and people to spread the word about data science in general and H2O.ai’s products in particular. I help people understand the use of products like Driverless AI and H2O’s open-source offerings. As Guy Kawasaki put it- Evangelism isn’t a job title; it’s a way of life. My work requires a lot of self-awareness and willingness to stretch and grow in the role.

What's the one thing that you see AI transforming completely? (Eg. cars becoming self-driving)

Transportation represents an area where AI has the potential to bring major changes. Autonomous vehicles—cars, trucks, buses, and drone delivery systems equipped with capabilities like lane-changing systems, sensors for collision avoidance, LIDAR systems etc can prove to be a potential gamechanger. Having said that, it is also important to keep in mind that there is still a lot of effort required to reach there.

Your biggest AI nightmare?

Machine learning models are being increasingly used to make decisions that affect people’s lives. With this power comes a responsibility to ensure that the model predictions are fair and not discriminating. We need to carefully think about the consequences of our model that can inform what kind of errors matter to us. Machine learning has proved its mettle in a lot of applications and areas. However, one of the key hurdles for industrial applications of machine learning models is to determine whether the raw input data used to train the model contains discriminatory bias or not. This is an important question and may have ethical and moral implications. 

What's your advice for other women who wants to pursue a similar journey?

Today a lot of support groups and platforms are emerging to help and support fellow women in the field of ML and DS. Companies are acknowledging the value that women bring to the Data Science teams. Having said that it will still require a collaborative effort from society to make diversity and inclusion a vital part of the ecosystem. However, I believe we as women need to be more vocal and participative and should encourage each other in the journey. Also, it is important for data science aspirants to keep obtaining new skills and carry out meaningful projects. Application is a very important aspect of Data Science. Online courses can help you get to know a topic but real understanding is achieved only when you apply the concepts in real-time. Here are some of the ways which I used to put the learnings into practice:

• Start writing a blog
• Try answering questions on forums
• Volunteer to speak at Meetups
• Use GitHub to host and share all your analysis

These activities will not only help to enhance one’s skills but also gives a lot of visibility in this field.