To obtain a job as an Enterprise Data Scientist, you must first show your value
Although this appears to be another cliche, this advice was meant to start your journey as an Enterprise Data Scientist, not to add it at the end as an afterthought. Doing so risks being unemployable.
As a Senior Data Scientist and Technical Lead for my organization, I often get pinged from data scientists looking to get hired. As senior folks, we’ve heard all of the initial conversations when job seekers approach us:
“I need a job, can you help me?”
“I just finished my bootcamp, and I’m a data scientist like you- do you have a job for me?”
“I’m expert at predictive modeling and can provide your company with the optimal model metrics.”
“I know all of the R and Python libraries for analytics and machine learning, and I can bring those skillsets to your company.”
If you don’t get the big picture, all of these initial requests are fraught with concern (and some cases dismay) to the hiring manager (who are either senior data scientists or have them on their team).
When you are employed in an Enterprise setting, business clients don’t care about your data science prowess. They don’t even care about your outputs from your ML model. They want to know if our data science service can help them solve their business problems, hence having business impact. Even in early maturity companies in AI utilization, business already have their analytics teams working on reporting dashboards for trend analysis and some basic predictions and forecasts about $ and how to get more of it. To think that in such scenario that you would get your ML outputs on a separate Enterprise AI application for business use and executive decision making is a stretch- if it has business impact, it will be part of an existing Enterprise application. And if you’re a super duper data science team with business, data organization, and IT support, you can deploy it as an Enterprise AI application.
So when you’re looking for a job at an Enterprise organization, you have to convince the hiring manager that you can help solve the business’ problems. That’s where you start to align your previous training and experience to what the business wants. When talking to the hiring manager, discuss your previous data science and analytics projects. While the technical knowledge is important and necessary, after demonstrating that, you focus on the problems that your project solved, and map it back to some of the problems to be solved at the company you are interviewing at currently. That’s why it’s important to have knowledge about the company’s domain and industry vertical. Ideally, you would already have that domain expertise. However, if you don’t, then do your due diligence and research that domain, and talk to experts in that industry vertical. That way, when you approach a hiring manager, your domain knowledge reassures them that you are not way off base by contacting them for a job in their industry vertical. Also, research the company’s mission, and try to brainstorm some of the ways you can help with that mission as a problem solver.
When business hires data scientists, they know what they can do- they know data scientists can help with business processes and problems, take their data from those processes and problems, and make sense of it for them. And when you can start identifying patterns of interest to them, explore hypotheses in a systematic way, identify use cases most appropriate for AI/ML, and help to build complete ML systems to contribute outputs to Enterprise applications, you will be well on your way to securing a job as an Enterprise Data Scientist.
So when the hiring manager asks you about how you will add value to the company, the above recommendations can help you prepare to answer those important employment questions. As you become successful as an Enterprise Data Scientist, you get to the point where you call the shots where you want to go, and the very successful ones become “unrecruitable.”
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, Senior 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. We will also overlay an MLOps framework to manage the AI solutions you build on this platform. If you don’t have an MLOps team, we 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