You’ve been working as a Data Scientist for a few years, and your goal is to reach the next level. Excelling in your current Data Scientist role is critical, but in many organizations, it alone won’t propel you forward. You’ll need to do more – or differently. In this article, I aim to provide you with valuables ideas and examples that can guide you toward operating as a Senior Data Scientist. Whether you are chasing that internal promotion or considering external applications, I hope this will help you succeed!

Data Scientist vs. Senior Data Scientist
Although every organization will have nuances in how they define the different levels of Data Scientists, there tends to be a consensus among many on the fundamental responsibilities and scope of work associated with each level.
In this article, I’ve outlined the differences in scope of work for a 3-tier hierarchy of data scientists. Some organizations have more granular levels, but I believe that the career progression I’ve described holds true in many companies.
Decoding the Data Scientist Hierarchy: From Junior to Senior – What Sets Them Apart?
Mastering the technical Data Science skills is a given to become a a Senior Data Scientist. With several years of experience, your technical toolkit should have grown substantially, encompassing a diverse array of data manipulation and preparation techniques as well as a broad repertoire of machine learning models that you can not only apply but also fine-tune, assess for performance, and understand their behavior. Additionally, your years of experience should have also given you a robust understanding of the business you are part of.
But this alone won’t necessarily take you to the Senior/Principal level.
You will need to grow the scope of your work to encompass more than the technical aspects of building data science solutions.
In particular, I am convinced that the five areas below will be instrumental to become the Senior Data Scientist you want to be:
- Re-think your finish line
- Know your stakeholders
- Generate opportunities
- Master the processes
- Become a teacher
1 – Re-think your finish line
"Lead data science project end to end"

Let’s take the example of a classification model as a piece of data science work. Let’s say a churn/attrition model – what would that piece of work look like?
- For a junior data scientist being assigned this work, their finish line would be a trained and validated model in a development environment. They’ll then work with seniors to QA and get to a stage of usable outputs.
- For a data scientist taking on the same work, their finish line can be that the trained and validated model has been tested in a QA environment and is productionized: the outputs are written in a production environment at a defined cadence. They’ll then work with seniors or manager to get these outputs to be used.
- For a Senior Data Scientist, the finish line looks different: not only they trained, validated, QAed, productionized the model, but they need to ensure the outputs are used by the stakeholders, and that the value of the model is measured. In our churn example, this could mean that a retention campaign or program is setup, deployed, the efficacy of using the model outputs is measured and communicated with the business, and a decision to roll-out or continue with the program is made.
A Senior Data Scientist project is successful only when stakeholders use it in their regular operations.
Although I’m using the "project" terminology, it’s essential to recognize that a Senior Data Scientist project is ongoing: once deployed and proven successful, the model usage needs to be monitored and refined, the model itself needs to be refreshed and tested periodicaly. This is why Senior Data Scientists might want to consider their projects as programs once they are implemented.
Often, the technical aspect of the program (such as model development or update) is the shortest phase. Collaborating with stakeholders on model deployment and ensuring its effective usage typically demands more time, effort, and coordination. This is where a Senior Data Scientist often has to put on their project management hat and take the lead in driving the program towards successful adoption and utilization.
2 – Know your stakeholders
"Establish relationships with broader cross-functional areas"

With several years of experiences, a data scientist has likely established valuable relationships with their immediate stakeholders – this could be the engineering teams, their regular stakeholders in Marketing, Strategy or Finance departments.
A senior Data Scientist should have demonstrated that they have pro-actively established relationships with broader areas within the organization.
Senior Data Scientists understand the business challenges that different departments face, they know what their ever-changing priorities are and how they operate.
Various methods to foster these relationships can be to setup regular chats or connection points with keys stakeholders, get invited to other departments regular performance meetings, or taking the initiative to introduce yourself to other teams and displaying genuine curiosity about their work and challenges.
A Senior Data Scientist also uses their understanding of stakeholders and deep business acumen to communicate effectively. They know their audiences very well and tailor their communication and presentation styles as well as the level of details to suit each situation. They begin by considering "what’s in it for them" before crafting their content, and they tell stories that resonates with stakeholders. For instance, they understand that the Director of Marketing prioritizes proven project or program value, while the VP of Finance is concerned with methodological robustness.
3 – Generate opportunities
"Identify and drive new projects opportunities"

Knowing stakeholders and their challenges allows the Senior Data Scientist to identify ways of supporting them. They are able to understand the business problem(s) that stakeholders face and pro-actively conceptualize data science solutions that would be effective, relevant and actionable.
As much as the "finish line" for a Senior Data Scientist is considerably further than the completion of the technical part, the "start line" for a Senior Data Scientist is positioned further upstream than data gathering or preparation for model building.
This is an "always-on" attitude where every interaction that a Data Scientist has with stakeholders or team members could lead to a new opportunity. A Senior Data Scientist always ask themselves "how could we use what we already have, or could we develop something new to do this better"
Once you have a clear idea of what you can do – and most importantly how it could be used and the value it would bring, it’s time to pitch the idea to your manager for approval. Following this, you can get back to the stakeholders to gauge their interest and initiate a more formal project. It can be beneficial to invest some time in building a preliminary prototype or proof of concept to illustrate your idea.
With a deep understanding of analytical/data science capabilities and the different business areas, the Senior Data Scientist is able to connect the dots and create opportunities to solve business problems or improve business functions.
4 – Master the processes
"Recommends improvements in processes and procedures"

The deep business understanding of the Senior Data Scientist extends to the processes and procedures of their organization.
Senior Data Scientists know how to request new data, what are the steps to ingest, integrate and model that data. They know the steps needed to productionize their outputs, are familiar with the deployment of these output into the various business platforms, and ensure that the results are accessible and actionable for business stakeholders.
Not only they know and follow these processes and procedures, they also identify improvements and implement gold-standards data, analytical and ML Ops processes to be followed by the entire data teams.
The Senior Data Scientist navigates the organizational structure effectively, leveraging the support functions available – project managers, business analysts, product owners – in order to drive their programs forward.
5 – Become a teacher
"Mentor and Coach Data Scientists and Data Analysts"

As mentioned in this great article, mentoring juniors or Data scientist has many benefits, and is a strong pre-requisite to be considered as a Senior Data Scientist.
A Senior Data Scientist is a point of reference for Data Scientists or Data Analysts.
By spending the time to support more junior team members, Senior Data Scientists foster a culture of knowledge sharing within the team, establish their credibility, gain trust, enhance collaboration and gain experience for potential future roles in people’s management.
Conclusion
We’ve seen that technical expertise is not going to be enough to take you to the Senior or Principal Data Scientist level.
A Senior Data Scientist pro-actively identifies data science project or products opportunities within the business and drives the business usage of these data science solutions rather than merely building them.
Increasing their visibility within the organization is also indispensable to the Senior Data Scientist: they act as a great team player by being a point of reference for Data Scientist and Data Analysts, understanding and supporting broad functional areas, their processes and stakeholders.
I hope this gives you ideas and direction on what you need to demonstrate for you to get up the ranks of Data Scientists! What do you think? Did I forget anything? Let me know in the comments below!
Sources
[1] G.Colley, Decoding the Data Scientist Hierarchy: From Junior to Senior – What sets them Apart? (2023)
Decoding the Data Scientist Hierarchy: From Junior to Senior – What Sets Them Apart?
[2] E.Berge, The Soft Skills You Need to Succeed as a Data Scientist (2023)
And don’t hesitate to follow me for more Data Science career/leadership content!
5 Ideas to Foster Data Scientists/Analysts Engagement Without Suffocating in Meetings