A Day within the Lifetime of a Senior Data Scientist Table of Contents Introduction Responding, Planning, and Meeting Updates Expected Data Science Work Summary and Personal Thoughts References



Including a rundown of a typical step-by-step project outline

Photo by Slidebean on Unsplash [1].
  1. Introduction
  2. Responding, Planning, and Meeting Updates
  3. Expected Data Science Work
  4. Summary and Personal Thoughts
  5. References

Target market:

This text is meant for individuals who are current data scientists and curious about becoming a senior data scientist. It may possibly also serve for example of working in data science normally for those trying to switch careers.

Let’s get the very first thing clear, a senior role is different from company to company, so please take this day-in-the-life with a grain of salt. The foremost difference between a senior and non-senior role, to me anyway, is that a senior position normally involves more collaboration cross-functionally between different teams and stakeholders. It can also be the case that you simply own a whole project, very just like a product manager, where you should not only coming up with the answer, but in addition coming up with the why and its impact, say KPIs (Key Performance Indicator), for instance. With that being said, let’s jump into an example of a typical day for a senior data scientist.

Photo by Alvaro Reyes on Unsplash [2].

Often within the morning

I’ll attempt to order these foremost events so as of time, but remember these can flip depending on the demand of the duty at hand.


You is perhaps surprised to seek out out that lots of your mornings or day for that matter, could be composed of responding to Slack threads. These discussions can range from anything like a straightforward clarification of information science jargon, to a long-detailed thread response backwards and forwards between multiple those who is more-or-less a brainstorming session on the subsequent steps for a selected project.

Slack or whatever tool your organization may use generally is a pro or con as it could possibly be distracting to some at different times, but it could possibly even be a quicker approach to answer questions than participating in a whole meeting, whether that be in person or on a video call like Zoom.


Chances are you’ll also not have any urgent notifications to reply to within the morning, but either way, it would be best to plan out the remaining of your day based on the priority of the asks. Sometimes your duties will actually be ranked based on the priority level, so you’ll have a transparent expectation of the order by which you’ll need to execute your tasks, whether that be an information request, or an extended project like obtaining features to your data science model. These asks can actually be a combination of others requesting from you to yourself organizing your personal requirements for a project.

Meeting Updates — Standup (as some firms call it)

Next, you’ll normally have some variety of morning meeting where you’ll update a stakeholder(s), manager, and other people in involved in a project. These meetings are vital in order that everyone seems to be on the identical page about what has been done to date, and what still needs work, and they sometimes are available in the shape of a small, quick meeting time so it’s as efficient as possible, not taking away time from others’ day.

Now that we’ve got a general concept of a typical morning, let’s give some specific examples of those events:

  • Answer clarifying, urgent Slack questions which are normally not needed to be an official task ticket — ex: “do you mind sending me the dashboard link for this evaluation”, “are you able to explain to me what MAE means?”, and “what are the foremost KPIs for this AB Test?”, etc.

  • Based in your responses, you would possibly create an official ticket (a preferred project management tool, Jira, for instance, which uses a Kanban board) — ex: there could have been a disagreement between the KPIs for a project based on disadvantages that arise, so a ticket might be created to investigate one other KPI that is probably faster and achieves similar, useful decision-making criteria

  • Updating stakeholders on what happened, what went fallacious (if anything did), what’s the blocker (if there may be one), what is required, and what are the subsequent steps — and who else must be pulled in to reply any outstanding questions — ex: we’re blocked on this project because there may be an excellent ticket to include a latest column right into a database table that is required for the information science model feature set
Photo by Jefferson Santos on Unsplash [3].

Assuming you might be using some form of ticketing system to arrange tasks, just like the aforementioned Jira tool, it would be best to update what’s in your backlog, in progress, and what’s now done. These facets is perhaps different in your organization. For instance, you would possibly have a planning section too. Crucial part is that your team is aligned on what each bucket means, whatever the title of the bucket and what number of you may have.

On this section, I can go a bit of more into detail on data-science-specific tasks, while the seniority aspect of it plays the a part of end-to-end project organization.

Project Outline:

Depending on the day, you would possibly perform the next, one+ tasks, with an example of every:

  • — ex: chances are you’ll notice a certain category of your organization’s product has low sales
  • — ex: “the pants category has the bottom sales out of any category”
  • — ex: “pants make up 80% of our inventory, yet it’s our worst performing category when it comes to sales”
  • (data science or mixture of) — ex: the pants category was incorrectly categorized since it didn’t discern between shorts and pants properly, so the inventory was incorrectly classified in ‘all-other’, and other people didn’t expand that category on the homepage. This incorrect classification was because the corporate was using a homemade rules-based solution. The answer is a call tree classifier categorization.
  • — ex: you’ll need certain data like an outline column in a database table to make use of as a model feature that can help the model classify appropriately, so that you will work with the information engineer to give you a strategy of ingesting product data
  • — ex: create an end-to-end strategy of ingesting a dataset, training and testing the model algorithm, and deploying the model endpoint in order that it could possibly be utilized by a service that routinely classifies any latest, incoming inventory, in addition to reclassifying ad-hoc, incorrectly categorized items. You may even make sure that the answer is definitely higher by proving it with real data, and that the machine learning operations process works as well.
  • — ex: you possibly can AB Test your data science solution using KPIs like ‘pants sales’ for instance, to see if the expected impact actually occurred. Chances are you’ll work with a separate AB Testing specialist at your organization or perform this yourself.
  • — ex: relay your findings from the test to stakeholders in order that everyone seems to be aware of the answer’s results.
  • — ex: depending on in case your test was successful, you’ll execute your production-ready solution to all the items moving forward. If the expected final result didn’t occur, you could possibly iterate on the answer based on those findings appropriately — possibly you overfitted your model, or have to train with more data so your model generalizes higher, for instance.

These foremost bolded points normally can account for a lot of the process you’ll see on a day-to-day basis depending on where you might be at along with your project. You may also be working on one+ project at a time. You may also be more involved with working with more stakeholders and executives at your organization in a senior role.

Every company has its differences and each role has its differences. Nonetheless, there are still commonalities between senior data science roles. To summarize, the foremost takeaway is that senior data science positions do all that a standard data science position entails, but additionally they concentrate on the project from end-to-end more and act as a product manager for that project, entailing more responsibility in the method as a complete.

The foremost things I learned as a senior data scientist are the importance of:

  • Communication
  • Prioritization
  • Willingness to pivot

Within the senior position, you’ll have more interaction with product managers and company executives, so with the ability to communicate clearly and efficiently is a must. Next, prioritizing your tasks and foremost data science projects is incredibly vital because you’ll discover that there may be this unlimited amount of labor you possibly can do, nevertheless, it is vital to know how one task or project compares to a different. Lastly, whilst you intend your roadmap, you’ll have for use to pivoting to a distinct project as priority and urgency can change around a certain topic. These three facets of the role are especially prevalent within the senior position since you not only serve because the subject-matter expert, but you furthermore mght are likely to serve because the leader of the information science space normally in your organization as well.

The largest thing that surprised me in regards to the senior position is that you’ll not be doing as much data science as you’ll expect. As an alternative, there are other parts of the business which are just as vital, like what I highlighted above that may take quite a little bit of day out of your day.

What motivates me is seeing the impact the senior position has on the business, as making a case for a certain data science project backed by a data-driven strategy and leading it from end to finish, is each vital and empowering.

The main focus in additional on the strategy, and here’s what we discussed:

Responding, Planning, and Meeting Updates
Expected Data Science Work

I hope you found my article each interesting and useful. Please be at liberty to comment down below in case your experiences are the identical or different as a senior data scientist. Why or why not? What other things do you’re thinking that needs to be discussed more, including more pros and cons? These can actually be clarified even further, but I hope I used to be in a position to shed some light on what to anticipate on this position.

Please be at liberty to envision out my profile, Matt Przybyla, and other articles, in addition to subscribe to receive email notifications for my blogs by following the link below, or by , and reach out to me on LinkedIn if you may have any questions or comments.



(I’ll receive a commission in the event you join for a membership on Medium)

[1] Photo by Slidebean on Unsplash, (2020)

[2] Photo by Alvaro Reyes on Unsplash, (2018)

[3] Photo by Jefferson Santos on Unsplash, (2017)


What are your thoughts on this topic?
Let us know in the comments below.


0 0 votes
Article Rating
1 Comment
Newest Most Voted
Inline Feedbacks
View all comments

Share this article

Recent posts

Would love your thoughts, please comment.x