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Alumni Career Advice

Q&A with Data Scientist Jiong Chen ’10

By Jiong Chen ’10

April 28, 2023

Tags STEM

How did you get started in your field? 

As a math major at Hamilton, my interest in data science started my sophomore year during my first probability class. I found the subject fascinating as it allowed me to predict how events can happen, and also how likely I can win a Texas Hold’em game. When I decided to go to graduate school, my math professors strongly recommended statistics for its practicality and  numerous real-world applications. I took their advice and received a master’s degree in statistics and a Ph.D. in biostatistics.

While studying for my Ph.D. I realized I am more interested in solving real-world problems with direct and measurable impact than academic research. So I started to look for industry internships. For a biostatistics student, the common internship opportunity is in pharmaceutical companies or at the National Institute of Health. I also applied for data science positions, a trending career option at the time. I got offers from both industries and decided to take the data science offer as the work was mentally challenging and the intern salary was generous. This internship led to a full-time offer and I became a data scientist after graduation.

What do you do during a typical day/week? 

During a typical week, I normally start by meeting with my stakeholders to brainstorm on how we can convert a real-world business problem into a statistics problem and make fine adjustments based on their feedback. Then I usually do research to locate and prepare the data for use in my analysis using SQL. After the data is ready, I will start building statistical models in Python or R to either identify insights from the data or create a prediction model. Then I document my findings and share with my stakeholders. Depending on the application, sometimes I need to recreate the model in a production environment if the output needs to be accessed in real time or on a scheduled basis.

What skills are most essential to be effective in your job? 

There are three important skills for data scientists:

Statistics and Data Analysis: Data scientists need to have a strong foundation in statistics and data analysis. This includes the ability to prepare data for analysis, conduct statistical analysis, and use machine learning techniques to create predictive models. They must be able to extract meaningful insights from large and complex datasets and communicate findings to stakeholders.

Programming: Data scientists need to have strong programming skills in languages such as Python, R, or SQL. They must have the skills to write efficient and maintainable code to manipulate and analyze data. They also need proficiency in working with big data technologies such as Hadoop and Spark.

Problem Solving and Business Knowledge: Data scientists are experts in their domain, they need to identify and define complex problems and come up with creative solutions to solve them. They must apply statistical and machine learning techniques to real-world business problems and evaluate the effectiveness of different solutions. They also need to communicate their findings and recommendations to non-technical stakeholders.

In addition to these skills, data scientists also need to have strong communication and collaboration skills, as they often work with cross-functional teams and need to explain complex concepts to non-technical stakeholders. They should also be curious and willing to learn new techniques and technologies as the field of data science is constantly evolving.

What are the greatest challenges you deal with? 

The greatest challenges usually come from stakeholders as data scientists require their buy-in for our work to have the most impact:

Understanding Stakeholder Needs: Understanding the needs of stakeholders is crucial for data scientists. This requires good communication skills, active listening, and the ability to translate technical concepts into non-technical language.

Managing Stakeholder Expectations: Stakeholders may have high expectations for the results of data analysis. It is important to set realistic expectations and communicate the limitations of the data and analysis.

Balancing Trade-Offs: Data scientists must balance competing priorities when working with stakeholders, such as the need for accuracy versus the need for speed, or the need for a deep dive into a specific problem versus a broader analysis.

Collaboration with Cross-Functional Teams: Collaboration with cross-functional teams is essential to the success of data science projects. Data scientists must work effectively with colleagues from different backgrounds, such as business analysts, engineers, and product managers.

What do you find most rewarding about the work itself? 

As a data scientist, the impact of my work is directly measurable. The adoption of any new model will directly affect business performance. Data scientists first use AB testing to measure the incremental benefit of using the new model versus the existing method. Once we find the results of  the AB testing proves a sizable lift in the business metrics, the new methods can be adopted by the business. The quantifiability of my work is the most rewarding as I can calculate exactly how much impact I have made from my contribution.

What do you think employers in your line of work look for in the people that they hire? 

Employers in data science typically look for the following qualities in the people they hire:

Strong Analytical Skills: Data scientists need to have strong analytical skills to quickly identify patterns, trends, and insights from complex data. 

Mathematics and Statistics: Data scientists must have a solid understanding of mathematics and statistics, including linear algebra, calculus, probability, and statistical inference. Data scientists also need to have a deep understanding of machine learning algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning.

Programming Skills: Data scientists need to be proficient in programming languages such as Python, R, or SQL, as well as tools like Jupyter Notebook or PyCharm. There is usually coding testing during a data scientist interview.

Business Acumen: Employers want data scientists who understand business principles and can provide insights that drive business decisions.

Communication and Collaboration: Data scientists must effectively communicate and collaborate, explain complex data concepts to non-technical stakeholders, work effectively with cross-functional teams, and manage a project from beginning to end.

Prior Experience: Employers may look for candidates who have prior experience in data analysis, machine learning, or related fields.