Ankit Sirmorya is an ML Engineering Manager at Amazon and has led several machine-learning initiatives across the Amazon ecosystem. Ankit was awarded the 40under40 Data Scientists Award by AIM for the year 2022. He was also a finalist of the UK’s prestigious ‘Computing AI Professional of the Year’ award. 

He holds an MS from the University of Florida and graduated in computer science engineering from NIT, Raipur. 

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In an exclusive interview with Analytics India Magazine, Ankit spoke about his data science journey, the challenges he faced, his achievements and his thoughts on the current analytics space.

AIM:  What attracted you to the field of data science?

Ankit Sirmorya: I developed an interest in data science very early on owing to my passion for solving problems with data. While pursuing my undergraduate degree, I delved further into data science and cloud computing. I have since worked on numerous projects in a variety of industries including consulting, finance, social media, communications and environmental management. Growing up, I naturally gravitated toward the field of ‘data’ and keenly observed its evolution from spreadsheets to machine learning.

AIM:  How did your data science journey begin?

Ankit Sirmorya: Working on several data science projects, studying relevant courses and publishing papers—my undergraduate degree laid the foundation for my journey into data science. These endeavours, along with the wide adoption of AI, inspired me to pursue graduate studies in machine learning at the University of Florida, USA. At the university, I participated in many data science research labs conducted by renowned professors. In addition, Amazon’s Machine Learning University has given me the opportunity to learn further about data science.

AIM:  What does your role at Amazon entail?

Ankit Sirmorya: In my role as an ML Engineering Manager at Amazon, I oversee the proactive notifications team for Alexa Shopping. We have a team of rockstar software engineers, machine learning engineers and data scientists. Our team develops machine-learning-based solutions for Amazon Alexa customers in order to provide them with personalised notifications and a better customer experience. My responsibilities include collecting requirements; designing software features; explaining technical designs; and data science solutions as well as forming product strategies in collaboration with users, other technical teams and management. Additionally, I manage departmental resources, hiring, mentoring, enhancing and maintaining a high-quality data science and engineering team.

AIM: How important is it, in your opinion, for young professionals to start early and develop their portfolios before venturing into data science?

Ankit Sirmorya: Before venturing into data science, one should develop portfolios to ensure success in the field. Strong foundational concepts in programming and data science are essential. It is important to acquaint oneself with a programming language, exploratory data analysis, machine learning algorithms and eventually build a data science portfolio. By learning these skills, it is possible to excel in the field of data science. 

AIM: How were you able to overcome the challenges in your journey into a rapidly evolving field like data science?

Ankit Sirmorya: Some of the biggest challenges in my career occurred while working on ambiguous projects that required machine learning to solve customer problems. To solve those problems, I started working backwards from the customer’s problem and applied machine learning techniques to come up with an extensible solution. By doing so, I was able to overcome the ambiguity associated with data science problems and find the optimal solution.

Another such challenge is multimodal communication where we need to interpret and communicate data science results to stakeholders in a manner that makes sense to laypersons. I was able to resolve it by linking the details of the technical solution to the business outcomes.

AIM: What do you consider to be your most notable professional achievements?

Ankit Sirmorya: In the course of my career, I was awarded several prestigious awards—the ‘40Under40 Data Scientists Award’ awarded by Analytics India Magazine, an enterprising publication renowned for its notable awards in recognition of innovators and achievers in the data science industry, in 2022 is one of them. I was also a finalist for the UK’s ‘Computing AI Professional of the Year’ award (2022). The Computing AI & Machine Learning awards recognise the best AI companies, individuals and projects in the field. The Asia Pacific Artificial Intelligence Association also honoured me with their membership. I have also undertaken the role of Community Lead for Global AI Hub, the world’s fastest-growing AI community.

In addition, I have founded a YouTube channel ‘Tech Takshila’ that focuses on scalable system designs and machine learning, and was able to build a community of ~5K subscribers. With over 70K+ views, the system design videos for Netflix, Uber and Instagram are some of the most watched videos on my channel.

AIM:  What would be your advice to anyone with a growing interest in the field of data science?

Ankit Sirmorya: My advice would be that once you acquaint yourself with the concepts of data science, there is a scope to level up. To learn and showcase your work in data science, focus on building a portfolio. When I talk about working on portfolio projects, I don’t mean building complex projects to accomplish that. For instance, a highly efficient recommendation system would be a good addition to one’s portfolio. This could be a small prediction model based on Titanic data or a housing price prediction model. Kaggle is a great place to learn how to build such projects. It offers resources to learn from leading data scientists all over the world. You will have a better insight into the standards and techniques used in the industry. 

Another effective way to build an amazing portfolio is to learn data science by building real-world projects. Through such projects, you would be exposed to real issues that’d help you understand concepts better in comparison to concepts learned through tutorials. If you are looking to solve real-world problems using data science, DrivenData is the platform to consider. It hosts challenges aimed to help resolve customer-related and environmental issues. Furthermore, communities such as DataKind bring leading data scientists together to work on socially beneficial causes and would be an excellent place for collaboration, building and learning from industry leaders.

AIM: What are the key skills you consider while hiring a data scientist?

Ankit Sirmorya: There are four hard and soft skills I look for in a data scientist hire and these are skills, I believe, one must cultivate to be a successful data scientist. 

• Statistical thinking: Since data scientists utilise data to generate information, they depend heavily on statistical knowledge. Data scientists’ work is arguably centred on knowing their algorithms and knowing how to apply them. 

• Technical aptitude: Data scientists write code and lead teams to create software, pipelines, packages, modules, features, dashboards and websites. Hence, it’s vital that data scientists cultivate technical acumen and have a hacker’s spirit.

• Multimodal communication skills: When an analysis is finished, the results are not usually an easy read. Although not unhelpful, they are often trapped in opaque readouts or in plots that make sense to an expert but not to the rest of the team and stakeholders. For algorithms to make the leap from the data science team to the hands of the rest of the company, they must be interpreted and communicated. 

• Curiosity: A good data scientist will implement requests and make confident predictions or analyses. Great data scientists will return time and again, asking for more data, interviewing users or trying something new because something in their work sparked their curiosity.

AIM: How do you see the data science and AI space evolving in the next couple of years? 

Ankit Sirmorya: As a result of machine learning’s immense contribution to data science, the field is expanding rapidly. With advanced personalisation, more relevant search engine results for users, code-free environments and quantum computing, the data science scenario has improved significantly. Data will continue to bolster businesses’ digital and e-commerce efforts, and our informed efforts can help steer the economy. According to Glassdoor, ‘data science’ ranks third in the list of best professions for 2021. With the industry’s continued growth, business leaders will strive to enable more opportunities for the growth of the field and its many professionals.