Land your Data Science job with our AI, ML and NLP Expert!

It is always important to learn from the best, and today we are going to be interviewing Sundeep Teki, one of our coaches, who has a diverse background in engineering, neuroscience, and AI across multiple countries, including working as a research scientist at Amazon Alexa AI. Sundeep is passionate about sharing his knowledge and we are delighted to hear his industry insights and tips to help you land your next dream job at the top companies!

Sundeep Teki has a diverse background spanning engineering, neuroscience, and AI across multiple countries in the US, UK, India, and France. He’s worked as an AI Research Scientist at Amazon Alexa AI and led an AI team at Swiggy (India’s DoorDash).

Coaching Approach: I tailor my coaching based on understanding the candidates’ needs, current backgrounds, and future goals.

Where His Candidates Work: I’ve helped candidates crack their dream jobs as data/research/applied scientists at Apple, Meta, Amazon, LinkedIn, Twitter, and other top tech companies.

What’s your background?

I initially trained as an electronics engineer as an undergraduate, which led me to appreciate that the brain is a highly complex electro-chemical network. I transitioned to neuroscience to better understand how the brain works and focused on the neuroscience of sound, time, language, and memory. This was an exciting and fulfilling phase where I conducted multiple studies to understand how neural networks in the brain encode sensory information, learning, and memory.

How did you get into AI?

Having worked as a Neuroscientist (as a Postdoc and Ph.D. candidate) for over a decade at Oxford and University College London, I became fascinated by the profound learning revolution that played out in the 2010s.

I was particularly excited at the prospect of applying my knowledge of biological neural networks to artificial neural networks for speech and language. This led me to my first job in the AI industry as a Research Scientist at Amazon Alexa AI in Seattle HQ, where I worked on deep learning for speech and natural language processing. I trained deep neural networks for speech recognition on thousands of hours of data to improve the existing state-of-the-art models for Alexa. I also built and deployed business-critical NLP classification models to detect offensive and sensitive utterances during conversations between users and Alexa.

What made you want to switch from academia to pursue AI in the industry?

As an academic, I was focused on fundamental brain research to advance the current understanding and knowledge of how the brain encodes speech and language. I also performed a few studies focused on understanding how the brain networks are adversely affected in hearing disorders such as tinnitus or hearing loss. Over time, I learned to appreciate that doing work that directly creates a more immediate impact for people, like clinical patient groups, motivated me more than doing basic research. This quest for driving a more significant impact on people’s lives led me to consider my industry.

Working at Amazon Alexa’s AI org was a perfect fit. I learned about the technological and engineering aspects of scalable AI systems while applying a product mindset to innovate for the customer. I haven’t looked back since, and in my next role, I led a used AI team at Swiggy (India’s Doordash), building NLP and Voice AI products.

Coming from a research background, what data science skills did you need to hone?

My training as a neuroscientist in academia empowered me with several transferable skills that are vital for success in the industry - hypothesis-driven, first-principles thinking, experimental design, statistical analysis of large datasets, communication, and collaboration with multiple stakeholders.

My research background thus provided a strong foundation for a role as an AI research scientist. However, to indeed be comfortable as an industry research scientist, I needed to upskill in topics related to software engineering topics like algorithms and systems design. On the AI front, I honed my skills in Python and learned to develop and train machine learning and deep learning models from scratch for various use cases.

What advice do you have for people looking for data science jobs right now?

If you have a quantitative background in computer science, engineering, physics, finance, and related disciplines, you already have the core technical skill set to transition and excel in data science.

Candidates from a non-technical domain, on the other hand, have the advantage of domain knowledge. Doing well in data science requires a deep understanding of both the data (and the business domain) as well as the scientific aspects of analyzing data. I have seen and coached several candidates from non-traditional backgrounds in transitioning to data science and becoming successful practitioners and experts in the field. My general advice to candidates interested in data science is to realize that they might already have several skills relevant to the data science industry. You only need to bridge the gap in the skills you lack or are less confident in to crack jobs at top tech companies and startups successfully.

What specific actions can candidates take to land data science jobs?

It is essential to build a public portfolio of projects relevant to your target companies (and their business domain) so that you already come across as an attractive candidate to the hiring managers and teams.

As you start applying for jobs and near the on-site interview stage, it is beneficial to practice mock interviews with experienced coaches who can give you personalized feedback to improve your interview performance. I consider interviewing a skill that needs to be polished before you go to the on-site interviews.

Practicing the different kinds of interviews in a mock setting, such as data science depth and breadth, coding, algorithms, software design, machine learning systems design, product/business cases, and behavioral interviews gives you a lot of confidence before you face challenging real-world discussions.

Where do you think AI is going in the future?

The field of AI has changed dramatically over the last decade. Consequently, the role of a data scientist has also transformed and evolved into multiple specialized roles like data engineer, machine learning engineer, research scientist, applied scientist, AI product manager, and so on. I believe that we are still in the early days of AI, and it is as good a time as ever to break into data science.

Data science is also becoming more engineering-focused as companies realize that business value cannot be realized until a robust infrastructure is in place to deploy, monitor, and maintain data science models in production. As a result, data science offers an opportunity for software engineers to transition laterally and work more closely with data and models apart from code.

Additionally, data science has matured as a field with the advent of several tools and products that make the entire data science life cycle more efficient, transparent, and reproducible. The organizational time, effort, and resources needed from conceptualization to production of machine learning models are reducing, enabling data scientists to drive more significant business impact.

Another trend is the focus on deriving business value from massive amounts of unstructured business data like images, text, audio, and video apart from structured, tabular data. For such applications, deep learning models are particularly relevant. We are currently witnessing a tremendous amount of innovation and advances in this area, with groundbreaking models like BERT, GPT-3, DALL-E, Imagen, and Whisper, to name a few.

We will see a more significant business impact of innovative AI R&D where startups and large companies leverage these technologies to build new products and services. It is, therefore, even more exciting to be at the forefront of AI innovation and build a long-term career in data science and AI.

Watch Sundeep’s mock interview videos on the Interview Query YouTube channel:

Amazon Deep Learning Interview: Justify a Neural Network

Robinhood Machine Learning Interview: Identifying Good Investors

Sundeep specializes in Artificial Intelligence, Natural language processing (NLP), Machine Learning, and A/B Testing. You can learn more about his work at SundeepTeki.org.

Our goal here is to prepare you for the toughest interview questions and help you feel confident in landing your dream job. Sundeep is best at helping you overcome your interview fears, by providing you concise knowledge of the industry and tackling any unsurety that you might have.

Visit our coaching page to book your first session!