Introduction
My name is Nischal Subedi, and I was born in the scenic city of Pokhara, Nepal. Due to my father’s frequent relocations for his finance career, I spent most of my formative years in Kathmandu, the vibrant capital city. These varied early experiences from constant relocation enriched my perspective and fueled a lifelong curiosity
As a child, I developed a deep fascination with computer video games, immersing myself in classics like Age of Empires 2 and Warcraft III. This early passion for gaming naturally led me to explore the world of technology, where I spent countless hours learning about game patches, customizations, and the intricacies of how things worked behind the scenes.
I was fortunate to have access to the internet during its rapid growth, which, coupled with my love for computers, led me to discover Western music, movies, and TV shows, often through torrents. I still fondly recall watching TV shows like The Big Bang Theory and movies like The Shawshank Redemption. In music, I became particularly drawn to rock and metal genres, which inspired me to learn to play the guitar.
I completed my A-levels (high school) in Kathmandu under the University of Cambridge, focusing on finance, economics, and mathematics. Inspired by early experiences with technology and Western culture, I pursued a degree in the U.S., choosing economics as my initial major—a decision influenced by my dad’s career. Although I received offers from several schools with partial funding, Troy University became my default choice as it offered full scholarship. Despite my initial concerns about its ranking, attending this university proved to be the best decision, as the scholarship allowed me to focus on academics, extracurriculars, and personal growth, and provided invaluable experiences and connections.
During my undergraduate years, I quickly realized that mathematics was foundational for many fields, including economics, which was my original major. Consequently, I decided to major in mathematics and minor in computer science. I initially concentrated on pure mathematics and participated in an REU program at Auburn University. However, I soon developed a stronger interest in applied mathematics, which led me to take additional statistics courses and briefly explore actuarial science. Although I passed Exam Probability, a key component of the actuarial exams, I found the field too restrictive. This insight motivated me to pursue advanced studies in Applied Statistics at the University of Delaware, where I also worked as a Graduate Teaching Assistant as well as interned for Barclays Bank Delaware as a Strategic Risk Analyst.
Currently, I am a data scientist at Home Partners of America, a Blackstone-owned single-family real estate company, focusing on pricing strategies. Since completing my master’s degree, I have been living and working in Chicago, IL, where I continue to grow and apply my expertise in technology and data.
Data Science Fellow
career options
Data science is a broad field with lots of career paths to explore. Here are some of the key roles 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 used extensively for programming, data manipulation, and analysis. Python libraries, which are collections of pre-written code, play a crucial role in enabling efficient workflows. Libraries such as Pandas and NumPy enable efficient data processing, while Seaborn and Matplotlib help with visualization. Additionally, machine learning models are built and deployed using frameworks like Scikit-learn, and statistical modeling techniques are applied to derive actionable insights from data. This comprehensive skill set allows for cleaning, transforming, analyzing, and modeling complex datasets effectively.
SQL is crucial for querying and managing relational databases. It helps me retrieve and manipulate data from various sources, ensuring that I can perform detailed analyses and extract valuable insights. For efficient database management, I often use the Dbeaver tool, user-friendly application that allows me to interact with a wide range of database systems. Dbeaver’s intuitive interface and comprehensive features make it an indispensable part of my data analysis workflow.
Proficiency in Git is essential for version control, allowing me to manage code changes, collaborate with team members, and maintain project history. Version control systems like Git track and record changes to code, enabling developers to revert to previous versions, resolve conflicts, and work on multiple features simultaneously. This skill ensures smooth development workflows and effective teamwork.
Python libraries like Folium are used for creating interactive maps and visualizing spatial data. This capability is complemented by other visualization libraries such as plotly to present complex data in a comprehensible and engaging manner, aiding in decision-making and communication of findings. I find these tools to be invaluable in my work with spatial data and information visualization.
Day-to-day work involves closely working with data engineers to build robust data pipelines and dashboards. This ensures that data flows seamlessly through the system and is readily available for analysis. Additionally, collaboration takes place with software engineers to develop and integrate APIs, enhancing the functionality and accessibility of the data systems.
What are the main soft skills you use on a daily basis in your current job?
When facing complex challenges, it's effective to break them down into manageable components and apply analytical thinking to find solutions. This method is crucial for troubleshooting issues, optimizing processes, and developing effective strategies. For example, in my current role, I encountered a challenge with inconsistent county names across two datasets, which made a direct join impossible. By breaking down the problem and using a fuzzy join technique with Python, I was able to match county names based on similarity scores. This approach allowed me to merge the datasets effectively and perform accurate county-level analysis, despite the lack of unique identifiers. This experience underscores the value of analytical thinking in overcoming complex problems.
Effective communication is essential for conveying technical concepts and insights to both technical and non-technical stakeholders. For instance, I regularly communicate with internal and cross-departmental stakeholders, as well as BX stakeholders, to present key findings, explain methodologies, and collaborate on project goals. This ensures that everyone is aligned and informed throughout the project lifecycle.
Working collaboratively with data engineers, software engineers, and other team members is vital for building integrated solutions. For instance, I coordinate efforts, share knowledge, and leverage each other's expertise to achieve project objectives. This collaborative approach ensures that the team works efficiently towards common goals.
The ability to adapt to changing requirements, modern technologies, and evolving project scopes is crucial. Staying flexible and open-minded is essential, quickly learning and applying new skills as needed to address emerging challenges and opportunities. For instance, I had to learn and utilize new Python libraries such as Folium for map visualization and working with spatial data, as well as survival models for specific analytical needs in my current company as spatial data is common in real estate domain.
Effective time management is essential for prioritizing tasks, meeting deadlines, and balancing multiple projects. By organizing your workload and setting clear goals, you can maintain productivity and achieve high-quality results within specified timeframes. In my own experience, I handle multiple projects and deadlines by focusing on tasks with the greatest impact first. This involves prioritizing essential tasks before addressing other responsibilities. Additionally, I continuously adjust my priorities to adapt to shifting demands, ensuring that my time management remains effective and responsive to changing needs.
Nischal
’s personal path
Tell us about your personal journey in
Data Science Fellow
:
My transition into data science was seamless, thanks to a solid foundation in math and statistics. There was, however, a pivotal moment when my career path was uncertain. During my master’s studies, I seriously considered pursuing a PhD and received offers from a few programs. Ultimately, I chose to enter the industry directly, as I realized that my primary goal was to work in the industry, even with a PhD, and my interest in research was not as strong as I initially thought.
My internship as a strategic risk analyst at Barclays played a crucial role in refining my career focus. I discovered a keen interest in working with cutting-edge tools and technologies. The contrast between the traditional methods used by my internship team and my growing enthusiasm for innovative data solutions helped me narrow my search for companies aligned with my interests.
The combination of my past internships, work experiences, and advanced coursework in statistics, along with a thriving job market, positioned me well for opportunities at several companies. After interviewing with and receiving offers from a few, I was particularly impressed by Home Partners of America. The company's rigorous interview process and the potential for substantial personal and professional growth were key factors in my decision. Since joining Home Partners of America, I have embraced the opportunity to advance as a data scientist. The challenges and experiences I've encountered have deepened my passion for the field, allowing me to continuously refine my skills and progress in my career.
What would you tell your younger you regarding building your current career?
Align your work with project and business goals from the start. Embrace the complexity of real-world problems and understand that solutions often aren’t straightforward. Keep an open mind, stay curious, and be ready to adapt and learn continuously. Your ability to navigate ambiguity and connect data insights to tangible business outcomes will be key to your success.
For instance, in my current role, I had to design an A/B test for high and low premium pricing for properties. The test could not be "pure" because keeping higher-priced properties at a premium for study purposes would lead to significant revenue loss, forcing me to creatively balance business needs with analytical rigor.
Final thoughts & tips
Data science is an ever-evolving field that demands continuous learning and adaptation. If you have a passion for reading and acquiring new knowledge and enjoy applying cutting-edge techniques and skills to solve complex problems, this field is for you. It requires not only technical expertise but also patience and hard work. Beyond technical skills, understanding the business domain and crafting effective solutions are crucial. While many can run pre-built packages, the true value lies in deeply understanding the data and applying it effectively to real-world scenarios. This blend of technical proficiency and domain insight is what makes a data scientist stand out and excel.
Resources to dig in more
The Data Science Handbook
This book provides interviews with prominent data scientists, offering insights into their experiences, methodologies, and career advice. It’s a great resource for understanding real-world applications and career paths.
Towards Data Science
This Medium publication features a wide range of articles, tutorials, and case studies written by data science practitioners. It covers trends, tools, and techniques in data science. Towards Data Science
Deep Learning Specialization by Andrew Ng
Offered on Coursera, this specialization provides a deep dive into deep learning techniques, including neural networks, convolutional networks, and sequence models, taught by one of the leading figures in AI. Deep Learning Specialization
Machine Learning Street Talk
Popular podcast and YouTube series that delves into advanced topics in machine learning and artificial intelligence. Hosted by AI researchers and enthusiasts, it features in-depth discussions, interviews with experts, and critical analyses of recent developments in the field. The show is known for its technical rigor and exploration of cutting-edge concepts, making it a valuable resource for those deeply interested in AI and machine learning.
