Decoding the Data Scientist Hierarchy: From Junior to Senior — What Sets Them Apart?
Shedding light on the scope of work expectations for junior, mid-level, and senior data scientists
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Experience and technical expertise play a big role in defining a Junior or a Senior Data Scientist — but what are the business expectations for these levels when it comes to scope of work?
While many resources and job descriptions discuss the technical responsibilities associated with each level (or the time spent managing them), there’s a noticeable lack of clarity when it comes to the broader business expectations for data scientists at varying seniority levels.
In this article, I will shed light on the scope of work expectations for Junior, mid-level, and Senior Data Scientists, a framework that has proven invaluable in my managerial role — and could be useful for you too if:
You are building up a formal Data Science practice and you are wondering what roles and levels you need — I believe this could help.You are a Data Science practitioner hoping to advance to the next level (or make the argument to your boss you already are) — this should help too.
So — let’s dive in!
Junior/Associate Data Scientist
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The entry-level Data Scientist is typically for freshly graduate students with a Bachelor or Master’s degree in Data Science/Applied Statistics/Big Data or an experienced Data Analyst who has recently pursued data science certifications.
The Junior/Associate Data Scientist focuses on clear tasks rather than full projects. Often, the analyses or models are designed by their Manager or a Senior Data Scientist, who also set project timelines and cadence of deliverables.
A Junior/Associate Data Scientist knows the data well and leverages it to deliver analyses or models in response to well-defined tasks.
A Junior/Associate Data Scientist undertakes well-defined data science tasks with some guidance from a manager or senior data scientist.
Junior/Associate Data Scientist expectations:
Develops predictive models, runs advanced analyses with guidance from manager/senior data scientist.Plans work for the upcoming few weeks.Translates insights into business recommendations with guidance from manager/senior data scientist.Presents findings/technical work to peers and manager.Understands and establishes relationships with close cross-functional business areas and direct stakeholders.Learns organizational tools, processes and procedures
Data Scientist
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The mid-level Data Scientist has a few years of experience and is able to understand a business problem, design and lead an entire analysis or model to completion with minimal output from their Manager or a Senior Data Scientist. They share intermediary results and raise potential blockers, but otherwise solve technical problems independently.
With a good organizational knowledge and business acumen, they suggest improvements and identify potential opportunities for new data science applications.
A Data Scientist understands the business context, designs and executes data science projects independently.
Data Scientist expectations:
Identifies potential projects opportunities within cross-functional areas and refine/prioritize with manager/senior data scientistPlans work items and cadence of deliverables for the coming few monthsDesigns and develops predictive models and advanced analyses with minimal guidance from manager/senior data scientistTranslates insights into business recommendations independentlyPresents insights/recommendations/technical work to peers and to business stakeholders.Understands and establishes relationships with broader cross-functional areas and stakeholders.Supports Junior Data Scientists and Data Analysts within the organizationKnows and follows organizational tools, processes and procedures
Senior/Principal Data Scientist
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The Senior/Principal Data Scientist is a driving force within the analytics organization and the broader business. Through their deep understanding of the business functional areas and established relationships, they consistently identify opportunities for improvements or new data science applications that drive value for the business. They prioritize and drive these opportunities to utilization with minimal Managerial support.
A Senior Data Scientist identifies potential opportunities across broader cross-functional areas and plans, starts and delivers complex data science projects to test, deployment and utilization.
Senior/Principal Data Scientist expectations:
Identifies and refine potential projects opportunities within broader cross-functional areas with minimal guidance from managerSupport team’s roadmap building and refinement for the upcoming six monthsLeads data science project end to end with minimal guidance from manager: from ideation to business utilizationDevelops and owns the project roadmap, stakeholder management and deliverables.Translates insights into strategic recommendations and drives recommendations to deployment.Presents insights/recommendations/technical work to peers, business and executive stakeholders.Establishes relationships with broader cross-functional areas and stakeholders.Is a point of reference for Junior Data Scientists, Data Scientists and Data Analysts within the organization: mentors and coachesKnows and follows organization tools, processes and procedures. Recommends new tools and/or improvements in processes and procedures.
Note: The Senior Data Scientist scope above would be that of a “business” Senior Data Scientist. In some cases, it might make sense to craft a “technical” Senior Data Scientist role for a highly technical person to become a technical expert (going deep) rather than a business data scientist (going broad).
Summary
In the realm of data science, distinguishing between Junior, Data Scientist, and Senior Data Scientist roles goes beyond experience and technical expertise. It hinges on the scope of work and business expectations.
In essence, these roles can be summarized as managing “Tasks,” “Projects,” and “Products” :
A Junior Data Scientist manages Data Science Tasks.A Data Scientist manages Data Science Projects.A Senior Data Scientist manages Data Science Products.
Having outlined these scope of work differences and shared them with my teams has been really helpful in supporting Data Scientists’ growth and development. It clearly sets expectations on what is required at each level of the Data Scientist hierarchy so practitioners know exactly what they have to demonstrate for them to be promoted to the next level.
How have you been operating in your organization? Similar framework? Am I missing anything? Please share in the comments below!
References
[1] M. Crabtree, How to write a Data Scientist job description (2022), https://www.datacamp.com/blog/data-scientist-job-description
[2] H. Brooks, S. Gutierrez, The Difference Between Junior, Mid-Level, And Senior Data Scientist Jobs (2020), https://www.datascienceweekly.org/articles/the-difference-between-junior-mid-level-and-senior-data-scientist-jobs
Decoding the Data Scientist Hierarchy: From Junior to Senior — What Sets Them Apart? was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
