Introduction
Hello, I’m Pratyush Kundu, a New Yorker by choice and a data enthusiast by passion. I’m originally from India and moved to the bustling streets of Manhattan to study at NYU and chase my dream of becoming an Investment Banker. Fortunately, my dreams are not static, and I found my way to the intersection of Data Science and Finance, which has given me this opportunity to speak to you!
Growing up, I was always curious about how things work, which led me to develop a deep interest in technology and data. If there was a puzzle to be solved or a complex problem lurking, I was the first one to dive in, much to my parents’ delight and occasional dismay when I "fixed" the TV. When I wasn’t trying to annoy my parents, I would turn to music and dabbled in playing the piano and the guitar. Music became my escape, a way to express myself and explore creativity that was different from anything else.
My career journey has been like trying to navigate the New York subway system for the first time: a little overwhelming, slightly confusing but ultimately rewarding. My role as a Trading Operations Analyst right now is a blend of financial operations, data science and development which is representative of my journey so far. Every day, I get to play with data, build scripts, and streamline operations to make trading more efficient. It’s like being the conductor of a complex orchestra, where every piece of data and line of code must play in perfect harmony.
When I am not trying to optimize trading operations, you can find me still exploring music, trying to re-create my favorite recipes, going on a run in Central Park or just winding down by reading a book.
Data Science Fellow
career options
Data is the most valuable digital currency in today’s day and age, and data science career options vary vastly. I encourage you to find an industry or area of expertise you like and then finding a data science focused role within; here are few of the options you can consider:
Data Science Fellow
skills
What are the main hard skills you use on a daily basis in your current job?
Python is my go-to language for data analysis and automation. I use it daily to build models, analyze trading data, and automate repetitive tasks. I had an interest in programming from a young age and learnt python working on personal projects during high school.
SQL is essential for querying databases and extracting the necessary data for analysis. It's the language of data manipulation and is crucial in any data-driven role. I honed this skill by working on college and work projects that required efficient data retrieval and manipulation.
Git is crucial for managing code versions and collaborating effectively with team members. I use Git daily to track changes, manage simultaneous changes, and ensure seamless integration of new features. This tool is indispensable for maintaining code integrity and streamlining workflows.
Bash scripting is used to interact with an Operating System’s shell, which is crucial when working with various Linux Servers and Virtual Machines. I learned this skill on the job and use it daily to work with multiple machines simultaneously for tasks such as running and scheduling scripts, managing processes and resource optimization.
What are the main soft skills you use on a daily basis in your current job?
Explaining complex insights to different shareholders with varying levels of technical knowledge is an art. I have learned that no matter how brilliant your analysis, it will be useless if not communicated effectively.
The ability to adapt to new tools, technologies, market conditions and business needs is essential in my role. It's like being a chameleon, always changing colors based on the environment.
The ability to work cross-functionally with multiple departments and teams is very crucial in a data science focused role. I routinely work with developers, traders, quantitative strategists, accounting and administration to ensure smooth operations and manage risk.
Pratyush
’s personal path
Tell us about your personal journey in
Data Science Fellow
:
I initially came to NYC with a clear goal: to become an investment banker. The allure of Wall Street was hard to resist, and I chose the NYU Stern School of Business to pursue my ambition. However, midway through my undergraduate studies, I realized that the world of banking and consulting wasn't the right fit for me. It felt too constrained, lacking the creativity and analytical depth I was craving.
That’s when I discovered the fascinating world of data science and statistics. This pivot was like opening a door to a room full of endless possibilities, where my love for problem-solving could flourish and align with my endeavors in programming projects when younger. I changed my course’s concentration to Data Science and began stacking my internships to gain as much experience as possible in this new field. I immersed myself in coursework and on campus organizations which exposed me to the fundamentals, talking to industry experts and making specialized projects.
My first job out of college was with Natera, a biotech company, where I delved into analytics and learned the ropes of extracting meaningful insights from large datasets. From there, my journey took me to Citibank, where I worked on data engineering and development. This role was like being a behind-the-scenes conductor, ensuring that all the data flowed smoothly and efficiently. It was here that I became adept at managing complex and intricate problems.
Now, as a Trading Operations Analyst, I find myself in a unique position that blends data science, software development, and financial operations. Each day is a new adventure, filled with challenges that require a mix of technical skills and creative thinking. Whether it’s analyzing operational data to extract efficiencies and remove bottlenecks, automating trading processes or managing risk, my job constantly introduces me to new technologies and methodologies and allows me to continuously learn and grow my Python and SQL skills.
Looking back, every step of my career path has been about exploration and growth. I’ve learned to embrace change and not be afraid to seek out new opportunities. It hasn’t been linear, strictly premeditated, or straightforward, but it has been incredibly rewarding trying many different things and seeing what sticks. I cannot really predict what my career will look like moving forward, but I can ensure that you will find me still taking risks, learning and never regretting failure.
What would you tell your younger you regarding building your current career?
I’d say “Don’t stress so much about finding the ‘perfect’ career path. It’s okay to change your mind and explore different fields, roles and careers. Focus on giving your best in whatever you are doing now and trust your gut. You didn’t believe time travel was possible and here I am here talking to you!”
Final thoughts & tips
Remember that every career path is unique. Don’t be afraid to pivot if something doesn’t feel right or something more exciting comes along. If your career doesn’t follow a traditional path, doesn’t mean it’s the wrong one.
At the end of the day, it is what you are spending most of your time doing, ensure that it makes you get out of bed. Find some joy in everything you do! Be patient, stay curious, and keep learning.
Resources to dig in more
Simply Statistics
Run by three biostatistics professors, this blog explores the use of statistics in big data across various fields. It’s an essential resource for anyone looking to understand the statistical foundations of data science.
StatQuest with Josh Starmer
StatQuest is a Youtube channel that breaks down complex concepts in Statistics, Machine Learning into simple, easy-to-follow videos. Josh Starmer has a knack for using clear visuals to complement their explanations, making this an excellent resource for visual learners!
Data Skeptic Podcast
The Data Skeptic podcast explores data science, statistics, machine learning, and artificial intelligence in a way that is accessible to both experts and beginners. Each episode focuses on a particular topic and features interviews with industry experts, making it a great resource for learning on the go.
Kaggle Discussions
Kaggle Discussions is a reddit like platform by Kaggle (a Data Science projects and competitions website). Discussions has a thriving community where users share insights, discuss challenges and learn from each other; it is a goldmine of knowledge with threads ranging from beginner tips to advanced model boosting techniques.
