Data Scientist vs Senior Data Scientist. Here’s the Difference. | by Matt Przybyla | Towards Data Science

IntroductionData ScientistSenior Data ScientistSummaryReferences
Introduction
Data Scientist
Senior Data Scientist
Summary
References
This article is for current data scientists who want to move up in their careers as well as for people switching careers to the data science landscape and want to know what separates and defines a senior role. It is important to note that not every workplace or company will offer both of these positions; however, if you satisfy the characteristics of a senior data scientist, you may be able to prove to your company why getting that title is worth it for you and them.
The senior data scientist position does not just mean that you are a data scientist with a certain amount of years of experience. It is rather, a position that entails a difference in responsibility of the impact of your data science or machine learning models on the business that you work for.
Not only are there key differences between the two job titles, but there are also some interesting differences and overlaps between the amount of machine learning, business intelligence, and product management from each role. Below, I will discuss both my experience in the two positions as well as reference material on distinguishing the two titles.
A data scientist is someone who defines a business problem, pulls data, performs exploratory data analysis, develops a machine learning model, and ultimately executes the model to display usable results or predictions for the end-user. While both the normal and senior titles overlap considerably in terms of required skills, programming languages, and overall knowledge, the main differences lie in what the two titles focus on — namely their responsibility in each of their respective facets of the data science process. As you will later see in this section and the next, the role of a senior position focuses on the beginning and end parts of the data science process, while the non-senior position focuses more on the middle part of that same process. Below, I will outline these key defining characteristics. Overall, the main process, in order, is typically the following:
1. defines the business problem (SR)2. develops data (SR)3. exploratory data analysis4. builds the model5. explains results to stakeholders (SR)
While this setup is different for each company and its respective job descriptions, I believe it makes sense generally from my personal experiences.
The normal data scientist position focuses on model building and the senior position focuses on defining the statement and using that model as the solution, which will ultimately be described in a meeting with either senior leadership or the company board.
Here are the two parts of the overall process that a traditional, non-senior data scientist will focus on:
exploratory data analysis — once you have the data or dataset, you will look for missing values, test for normality, check if classes are balanced, feature engineer, and more to polish your input data for your model.
builds the model — the next step in the process is to build base models that do not focus on hyperparameters, but focus on whether the problem is supervised or unsupervised, (time series, etc...). Once you establish which model and library are appropriate to employ, it will be applied in your Jupyter Notebook or platform that houses all your exploratory data analysis. Next, you will perform iterations on that model and tune hyperparameters, and either deploy it yourself or rely on a machine learning engineer to deploy that model into production (once again, every company is different).
A senior data scientist can and should be able to perform all the tasks of a typical data scientist while also interacting with the business-facing side more often. You will collaborate with stakeholders, subject matter experts, and company leadership to get the most use and positive impact of the model on your business.
defines the business problem — while a traditional data scientist will focus on the model itself, the senior position will generally entail a proof-of-concept. You will explain to the business the value that the model will drive, as well as possible complications, needed resources, and who will interact with the predictions or results. This role can sometimes be seen as a product manager who organizes the process to make sure that specific collaborators are all aligned. When data science is performed in the workplace (as opposed to academia), you will be working with subject matter experts, software engineers, data engineers, and UX/UI designers. This part of the operation is somewhat ignored in school where the main focus is building the model.
You can build an incredibly accurate model, but if it is not helping the business by automating a previously manual process or driving value, then you are wasting time, money, and the model is obsolete.
The senior position will make sure that the entire model is effective and everyone stays on track.
develops data — once the concept of the model is proven by establishing the business problem statement and solution, you will then have to find your data sources. Similarly, this point is not stressed as much in academia and is an incredibly vital part of your position, as creating your dataset can take days or weeks. You may have to reference an API (Application Programming Interface) with software and data engineers or perform complex joins across databases and tables.
explains results to stakeholders — now that the statement is defined (think of this as a hypothesis as well), data is established, exploratory data analysis is performed, the model is built, you will now need a person from your data science team, usually at the senior level, to explain your complex results to non-technical users.
What if my company does not have a senior position available?
— if you have satisfied the conditions above, you should outline all of the steps you do in the data science process to prove to hiring managers or leadership that you deserve that senior title. If you are building the model and running the entire project like a product manager, you should be recognized for that as it is essentially two roles you are performing.
Yes, these descriptions and examples can be different at your company, but in general, from my experience, especially looking at hundreds of LinkedIn profiles and job descriptions, the consensus is that a normal, non-senior position focuses on the model and the senior position focuses on how that same model interacts with your business. Additionally, in some places, a senior position can be described as someone who just has more work experience in the number of years, but with that increased work experience, that data scientist will employ the facets of the process I described in the senior data scientist position above.
There is not necessarily an official word on the formal differences between senior and non-senior, but there are several articles and job descriptions that summarize this description similarly as I have here. Here [4] is an article outlining the senior data scientist position that is helpful.
I hope you found this article interesting and useful. Thank you for reading!
[1] Photo by Christina @ wocintechchat.com, (2019)
[2] Photo by Christina @ wocintechchat.com, (2019)
[3] Photo by Christina @ wocintechchat.com, (2019)
[4] Peadar Coyle, What Does it Mean to Be a Senior Data Scientist?, (2020)