Starting as an Enterprise Data Scientist
Tips for starting a new job as a data scientist for a large organization.
So you landed a new job as a data scientist for a large organization. Chances are, you most likely are coming from a startup company, or you just completed your formal training/grad school in data science. And now you’re an enterprise data scientist for a large organization.
No more class projects, where you are taking small data and everything you build fits on your gaming laptop or MacBook, or fits on the limited capacity servers provided for you by the university.
No more start up companies, where you hang out with all of the company employees, the 5 of you, consisting of a CEO, COO, SVP, and a couple of developers who have been coding since children. No more worrying about funding and paying yourself and employees. No more 100 hour weeks to complete AI prototypes that you pitch to multiple prospective clients. No more wearing all hats as data scientist, data engineer, ML engineer, cloud engineer, and software developer. No more coffee shops and cheap junk food.
You’re an enterprise data scientist. Suddenly, your classroom and hip incubator environments are exchanged for office towers and cubicles, and you scramble to find business casual work attire, as your closet is filled with swag from all of the hackathons you entered to hone your craft.
All of a sudden, you are inundated with company jargon and industry verticals where you have no familiarity or expertise. You see this thing called an organizational chart, and it is plastered on a large wall as a poster board, and this is only for your department. You end up on the bottom of that org chart as the new data scientist. You’re introduced to your coworkers, team leaders, managers, and find out you’re just on one of hundreds of teams sprinkled across headquarters and the satellite offices. And the data- you’re sitting on a gold mine of data- and you can’t make sense of how it’s all organized, the vastness of it. Yet it is what attracts you here, given your severe lack of data in your startup, leading to failed AI prototypes. You have arrived at your enterprise job of choice, but you’re overwhelmed.
But don’t fret- this was my experience years ago, and I can give you tips on what my mentor and I set in motion for me to be successful as an enterprise data scientist.
Even before you arrived here, you should have mapped out your personal career roadmap for your data science career. Sit down with your mentor and start to map this out. You need this personal career roadmap, especially for data science, as the career progression for data scientists are not set in stone as other professions. Without a personal career roadmap, it’s easy to get derailed, and can lead to being shut out of data science. Most of my classmates from my data science graduate program never made it as data scientists (most are software developers), so get to work on that personal roadmap and stick to it, revising as things change.
After completing your personal roadmap, start to work on an AI roadmap for your organization. This will take some time, as your first year will be spent networking with the various business lines and IT, and taking inventory of the current state, business problems/use cases and associated data.
For organizations with high AI maturity, then see how your AI roadmap aligns with theirs. Any differences could be due to your own lack of experience, or it could be an opportunity to help optimize it with your fresh ideas and requirements gathering from your new company.
For organizations with low AI maturity, then this is an opportunity for you to shine, by doing your current state analysis and seeing how an AI roadmap you devise could be applied to your new company. This will take time, as you need to build trust with your new bosses. And you build trust by delivering on small successes with AI prototypes (more on this in future articles).
After working on your roadmaps, you want to keep up with the latest developments in data science. You can do this on your free time. And when you are learning, go deep.
For immature AI organizations, they will have AI/ML platforms that are not ideal, and some still stay on premises. Focus on platform as a service, and get away from on-prem. Soon, on-prem will become obsolete, so you need to point your data science strategy to the cloud.
If you must do on-prem, then focus on Spark clusters. The data lakes associated with this will suffice to showcase data science algorithms. But you’re always thinking platform as a service and cloud-first for data science platforms, and you will be your organization’s cheerleader for this.
When you’re upskilling in your free time, focus on data science on Azure, AWS, and GCP. Earn AWS and Azure certifications for cloud and data science. Fresh data science knowledge will be your calling card. At the same time, you want to brand yourself as a data scientist, so whenever someone hears your name in the organization, they know you’re all about data science.
Network as often as you can, with other data scientists in the organization. You will most likely have to go outside your department to find them. It’s important to network, so you can work on shared struggles together. Otherwise, you all will just stay in your silos and you lose the community of practice aspect of data science that is crucial for data science to gain traction at an organization with low AI maturity. When I started years ago, I facilitated data science lunch and learns, where I invited data scientists in the organization to present their data science work. This had very positive effects on our community of practice, where none had existed before.
Finally, be careful who you bring in to your team, as you don’t want them to show you up. You worked hard to get this position, and your are building data science capacity for your team. Bring in other professionals only for specific projects you guide and build. After all, if you followed the tips in this article, then new people should be following your lead, as you were strategic and carefully planned your career as an enterprise data scientist!
If you're looking for support from me, here are a few options:
Enterprise Data Science Consultancy: With my consult team comprised of a Senior Data Scientist/ML Engineer, Senior Data Engineer, and Senior Cloud Engineer, we will help you architect and build your Enterprise Data Science platform, and transfer knowledge to your IT team to maintain and optimize it. If you don’t have an MLOps team, I will help you build one. Please get in touch about this consultancy here
Coaching and Mentorship: I offer coaching and mentorship; book a coaching session here
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