
Another common question I see among Data scientists is, "how do I transition into becoming a senior data scientist?" The status of senior data scientist appears to be very subjective and varies from company to company. There seems to be a wide variety of answers that detail what it means to be a senior data scientist. Here, I review and explore the title, as discussed by PayScale, Cleverism, Zippia, and KDnuggets.
Exploring the Title of Senior Data Scientist
Looking into what PayScale defines as a senior data scientist, it is clear that they expect Machine Learning, Python, and Big Data Analytics skills. These three areas correlate to individuals who are above average on the pay scale. A skill that pops up in Payscale’s analysis that others hinted at is leadership.
From there, I look into how Cleverism defines the role of senior data Scientist. Reviewing their definition, it is clear that a senior data scientist often oversees younger individuals on the team and is seen as a mentor to them. They are also the individuals who provide advanced expertise on the team for Data Science who looks to advance the adoption of analytics, tools, and techniques across the business.
Clerverism expands on their definition of a senior data scientist by breaking it down further into the categories of management, analytics, strategy, collaboration, and knowledge. Here they continue to detail how a senior data scientist works to guarantee the integrity, accuracy, and adequacy of the team’s work while working in a fast-paced environment.
Like PayScale, Cleverism also notes essential skills: Machine Learning, Python / R, and Data Mining. A critical skill that Cleverism points out that others did not is communication. Others expect senior data scientists to have exceptional communication skills as they collaborate and deliver results on their work. Communication comes in handy when taking complex messages, outputs, and algorithms and simplifying them into actionable insights for key stakeholders and management.
Continuing with Zippia, they note down traditional skills like Python, Data Analysis, Machine Learning, and Predictive Analysis with jobs typically focused on technology or finance. When looking further into the other skills listed, it seemed typical to find either specific technologies or programming languages in the mix. One skill that stood out in their analysis was on cloud technologies such as AWS or Azure, making sense with big data as it allows for more compute power to analyze the large quantities of data. In their article, Zippia notes that senior data scientists commonly come from statistics, computer science, and mathematics backgrounds, which seems consistent with the other sources.
Lastly, a post on KDnuggets by Mısra Turp describes the differences between junior and senior data scientists. This article starts nicely, stating that a common impression means that people holding a senior position are seen as knowing everything, but the truth is, you don’t. You are an expert to a certain extent, and then learning takes over. The field of data science is rapidly evolving, and there is always something new to learn. Along with that, being a senior data scientist is more than just possessing technical capabilities.
When you have reached a senior data scientist’s level, Mısra Turp states that you should have a more in-depth knowledge of the main concepts and techniques in data science gained from your work on different projects. You have reached a point where you have good experience under your belt and can learn more advanced topics. As well, Turp states you should be teaching and mentoring more junior colleagues, sharing with them your experiences so they can learn from them. Lastly, the article says that a senior data scientist is no longer just working on projects but leading them and being responsible for their success or failure.
Common Themes for Senior Data Scientists
After reviewing these different articles for what it means to be a senior data scientist, I have picked out some common themes:
- They have strong technical skills and can take full ownership of a significant feature in the backlog. They can work with key stakeholders and subject matter experts and perform any research necessary to implement their work. They have analytical and critical thinking skills.
- They can troubleshoot problem areas and investigate a solution that will fix the issue. If they are unsure of the problem, they can research and learn how to resolve the problem or collaborate with others to aid them in the effort.
- They can mentor younger or less experienced individuals and teach them what they have learned. They can lead other members who are contributing to their projects and work effectively with them.
- They can look at the "big picture" and understand where their value is for the business. They can analyze user needs, understand where the gaps are, and lead change across the company to adopt data science practices. They see the needs of the business, design solutions for those needs, and provide positive value back to the company.
- They demonstrate excellent communication and collaboration skills and can explain their work to both technical and non-technical audiences. They have good documentation for their work and attention to detail. They can work well with others from different teams to drive a project to closure.
- They take responsibility for their projects and can complete the work. They are held accountable for the success or failure of the projects they lead and can work with others to stay on schedule.
- Skills: Data Mining, Python / R, Machine Learning, Leadership, Communication, Collaboration, Big Data Analytics, Cloud, Predictive Analytics, Know How to Learn, Mentor
Final Thoughts
All in all, these different companies share many similarities and differences in the way they describe a senior data scientist. The specific requirements may vary company by company, but there are general themes that stand out. My suggestion is to find a niche area that you enjoy in data science and focus on developing your skills in that area. Work with your company and manager to understand the requirements to become a senior data scientist and determine how you can improve to achieve that goal.
How does your company define the role of a senior data scientist? What skills do you find valuable in the position?
If you would like to read more, check out some of my other articles below!
7 Lessons Learned from 7 Months of Remote Mentoring
Top 3 Challenges with Starting out as a Data Scientist
The Lazy Mindset of Effective Data Scientists: How Automation Can Help