My honest reflections after working in 3 different Data Science teams (hint: there’s quite a bit more PowerPoint than you think that)

Data Scientists have been called many things:

• “A Data Scientist is a statistician who lives in San Francisco”
• “Skilled modellers, but not like that”
• “I receives a commission to Google Stack Overflow”
• “I sell magic to executives”

Or, my personal favourite:

• “Data Science is statistics on a Mac”

As this smorgasbord of job descriptions shows, . A lot of the prevailing articles on the market — while excellent — date from 2012–2020, and in a field that evolves as fast as Data Science these can quickly turn into outdated.

In this text, my aim is to peel back the proverbial covers and provides a private insight into life as a Data Scientist in 2023.

By drawing on my experiences of working in 3 different Data Science teams, I’ll attempt to help three forms of people:

1. : I’ll give a practical insight into what the job involves, so you’ll be able to make a more informed decision about whether it’s for you and what skills to work on
2. : Spark recent ideas for things to try in your team and/or provide you with a method to answer the query “So what’s it you actually do?”
3. : Get to know what the heck we actually do (and, perhaps more importantly, what we don’t do)

The Head of AI at a big tech company once told me that the largest misconception he encounters about Data Scientists is that we’re at all times constructing deep learning models and doing “fancy AI stuff.”

Now don’t get me fallacious — Data Science can get very fancy indeed, but it surely encompasses quite a bit greater than Artificial Intelligence and its flashy use cases. Equating Data Science with AI is form of like assuming that lawyers spend all their days shouting “I object!” in court; there’s quite a bit more that goes on behind the scenes.

There’s more to it than “fancy AI stuff”

Certainly one of my favourite descriptions of Data Science comes from Jacqueline Nolis, a Principal Data Scientist based in Seattle. Nolis divides Data Science into three streams:

1. — “taking data that the corporate has and getting it in front of the fitting people”
2. — “taking data and using it to assist an organization make a call”
3. — which she describes as “taking data science models and putting them constantly into production,” although I might probably take a broader view and include the actual development of ML models.

Different corporations will emphasise different streams, and even inside these streams the methods and goals will vary. For instance:

• If you happen to’re a Data Scientist working in Decision Science, your day-to-day tasks could include anything from running A/B tests to solving linear programming problems.
• If you happen to’re a Data Scientist who spends most of their time constructing ML models, those might be either product-focused (e.g., constructing a suggestion algorithm which can be incorporated into an app) or business-operations-focused (e.g., constructing a pricing or forecasting model, used to enhance industrial operations in the corporate’s backend).

Personally, considered one of the things that I find most enjoyable about Data Science is attending to dip my toes in all three of those areas, and so within the Data Science roles I’ve done, I’ve at all times tried to make certain there’s numerous variety. It’s method to try to construct the “jack of all trades, master of 1” mindset that I’ve previously advocated for as a method to frame your profession as a Data Scientist.

Ah, PowerPoint. If you happen to thought Data Scientists were spared from it, how fallacious you were.

Making and presenting slides is a key a part of any Data Scientist role because your models ain’t goin’ anywhere should you can’t communicate their value. As Andrew Young puts it:

Through the years, I actually have seen many PhD-holding data scientists spend weeks or months constructing highly effective machine learning pipelines that (theoretically) will deliver real-world value. Unfortunately, these fruits of labor can die on the vine in the event that they fail to effectively communicate the worth of their work

In my team, we place a number of emphasis on stakeholder communication and so PowerPoint tends to feature quite heavily in our day-to-day jobs.

For each project, we construct a master slide deck which different team members can add to, after which we select relevant slides from this deck every time it’s time to present to stakeholders. Where crucial, we attempt to create multiple versions of the important thing slides in order that we’re capable of to different audiences, who’ve different levels of technical expertise.

If I’m being honest, I actually don’t mind spending time in PowerPoint (please don’t cancel me), as I find that making slides is an incredible method to distill your key ideas. Truthfully, it helps me remember big picture questions like: (1) what problem am I solving, (2) how does my solution compare to the baseline one, and (3) what are the dependencies and timelines.

It’s commonly said that data science is 80% preparing data…

… and 20% complaining about preparing data.

And I’m not only talking about corporations where Data Science is the “recent thing.”

Even in established corporations with established datasets, data preparation and validation can take a considerable period of time. On the very least, you’ll likely find that datasets are (1) stored on different platforms, (2) published at different cadences, or (3) in need of considerable wrangling to get into the fitting format. Even once your models are in production, it is advisable to be continually checking that your datasets aren’t drifting, breaking or missing information.

And .

In considered one of my old jobs, we had an internet form where users were required to input their address, and our users used 95 alternative ways of spelling “Barcelona”: I’m talking the whole lot from “barcalona” to “BARÇA” and “Barna.”

95 alternative ways of spelling “Barcelona”

The moral of the story: don’t have free-text fields unless you should spend your coming weeks crying over documentation.

Certainly one of the things I like most about Data Science is the proven fact that it involves continual learning.

For me, I’ve at all times dreaded the concept of getting stuck in a job where I just do the identical things on a regular basis, and I’m thankful to say that Data Science shouldn’t be considered one of those careers. As a Data Scientist, you’ll discover is that there’s no such thing as a “standard” project. All of them require a rather bespoke approach, so that you’ll at all times be needing to adapt your existing knowledge and learn recent things.

And I’m not only talking about “formal” learning like attending conferences or doing online courses.

More likely, you’ll spend a considerable amount of your days doing “micro-learning” by reading coding documentation, Towards Data Science articles, and Stack Overflow answers. If you happen to’re taken with how I approach the duty of continual learning and staying up-to-date, you may be taken with reading considered one of my recent articles where I speak about this in a bit more depth:

Data Scientists don’t exist in a bubble.

We’re embedded in teams, and to work effectively you’ve gotten to have the option to work together. I actually like the best way that Megan Lieu puts this:

The largest disappointment I had after I finally became an information scientist was learning that it’s not only heads-down work all day.

“I can’t wait to not talk over with anyone, construct models and just do technical data science-y things by myself on a regular basis!”

Much to my introverted horror, I noticed I not only needed to collaborate with, but in addition actually TALK to business and external stakeholders on a regular basis

While I feel slightly less strongly than Megan (I’m more of an extrovert by nature), I too was initially surprised by how team-based the role can often be. In my role, “collaboration” means things like: having day by day stand-ups to debate tasks and blockers, doing regular pair-programming sessions to debug and optimise code, and having well-balanced discussions (read: arguments) in regards to the merits of various technical approaches.

All in all, I reckon I spend about 50–70% of my time working solo and the remainder of the time doing pair or group work, although the precise ratio will depend quite a bit on your organization and level of seniority.

Thanks for reading this small insight into my life as a Data Scientist.

I hope you’ve found it helpful, and please be at liberty to achieve out should you fancy a chat 🙂

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