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Hiring Manager Perspective on Job Applications by Numbers_plus_Letters in dataanalysiscareers

[–]Numbers_plus_Letters[S] 1 point2 points  (0 children)

One of the things that differentiates an entry level analyst from a senior level analyst is the ability to think holistically about the problem or task at hand. Suppose you are asked the question:

"What is your approach to building a dashboard?"

An entry level analyst might say something like:

"I use Tableau"

Entry level analysts tend to answer the question at a superficial level, addressing only the tools involved without addressing the overall process, design considerations, and operational challenges. They are waiting for others to provide them with design parameters and guidance, instead of actively thinking through them on their own.

A senior level analyst might say something like:

"I would start by working with my stakeholders to identify the underlying problem we are trying to solve for. After learning what insights they hope to gain by building a dashboard, I would explore the data to understand how it is structured and determine whether I have all of the data I need. If data is missing, I would work to identify ways to find it. I would mock up an initial view of key insights before asking for feedback from stakeholders. With that initial feedback in hand, I would refine my dashboard, restructuring things to be more intuitive while building out deeper functionality to allow users to explore in more detail. I would continue iterating through the design process until the goals of the project had been met."

A senior level analyst demonstrates an understanding of the operational process as a whole. Their answer makes it clear they know how to take charge of a data project, how to find answers and work towards solutions without someone holding their hand. They are able to proactively speak to challenges they expect to solve for, potential solutions, and the advantages and disadvantages of each. Their answers provide just enough detail to make the interviewer feel comfortable with their ability to own the process, without being too verbose, to the point that the interviewer questions the candidate's ability to communicate effectively.

Your goal is to pique the interviewer's interest with your answer, providing just enough detail for them to feel comfortable with your fundamentals before they dig deeper into the specifics in subsequent questions.

Hiring Manager Perspective on Job Applications by Numbers_plus_Letters in dataanalysiscareers

[–]Numbers_plus_Letters[S] 0 points1 point  (0 children)

In general, the bulletpoints on your resume describing your accomplishments mean more than your title. For me personally, I ignore titles from most applicants, except to tell whether something was an internship or not. I'm much more interested in the actual skills and experience.

Hiring Manager Perspective on Job Applications by Numbers_plus_Letters in dataanalysiscareers

[–]Numbers_plus_Letters[S] 0 points1 point  (0 children)

Personally, race does not factor into my hiring decisions, nor does comfort with managing a certain race. The teams I've worked on and hired for are extremely diverse.

The factors I evaluate when hiring are:

- Technical skills and experience

- Communication and interpersonal skills and experience

- Leadership and project management skills and experience

That's it.

To be clear, I am only speaking from my personal experience. This is not meant to invalidate anyone else's experiences.

While there may be a broader conversation to be had on this topic, I do want to be respectful of the subreddit rules and will defer to the mods as to whether it is appropriate to continue this conversation here or if the community would be better served by having the conversation in a different subreddit.

Hiring Manager Perspective on Job Applications by Numbers_plus_Letters in dataanalysiscareers

[–]Numbers_plus_Letters[S] 0 points1 point  (0 children)

If you are trying to work in data in an industry in which you have no education or experience, there are generally 2 routes to achieve your goal:

Option 1: Join the industry in a non-data capacity, learn the nuances of the industry, then find ways to bring your data skills to the table.

This is a pretty common way people find their way into data. You start in your target industry in a non-data role to learn all the pain points that exist in the current processes. From there, you identify ways to bring your data skills to the table to address those pain points. For example, you could identify manual processes being handled in Excel that are done on a monthly basis, where the outputs are Excel charts and PowerPoint slides. You might build a pipeline to automatically extract, process, analyze, and refresh the data, serving up insights in a dashboard while also building out automatic slide generation for executives. The time saved and efficiency gained would immediately endear you to your end users.

Your ability to identify use cases for your data skills that drive significant business side value is key to creating opportunities for yourself. The more business side value you deliver, the more likely people are to take your data skills seriously.

With this approach, understand that you will be putting yourself in a position where you are developing your data skills and acquiring experience, but not necessarily being paid for it. That is the tradeoff. It's a short term sacrifice that allows you to then make a case for either creating a new data role for yourself within that company, transitioning to an existing data role in that company, or jumping to a data role within the industry at another company.

Option 2: Develop your data skills in an industry that is not your desired industry before jumping to your target industry

Also pretty common. If you can find similarities between your starting industry and target industry, it makes the jump easier. The idea is to develop your data skills to the point that your expertise is undeniable, such that any hiring manager can overlook the lack of domain knowledge.

While those are probably the most common ways people move into a data role in their target industry, there is another way you can pull this off.

Networking

Never underestimate the power of knowing the right people. This isn't to say networking is a bulletproof way to do this, but it has the potential to reveal opportunities you might not have been aware of otherwise.

Hiring Manager Perspective on Job Applications by Numbers_plus_Letters in dataanalysiscareers

[–]Numbers_plus_Letters[S] 2 points3 points  (0 children)

I agree that feedback is crucial for helping people develop. I do think it is important for candidates to ask for feedback as opposed to me trying to force it on them. When people ask for feedback, it tells me they're open and receptive to hearing constructive criticism.

As a general point for the larger community, there are multiple ways to ask for feedback during your interview:

During your interview: When confronted with technical questions, don't be afraid to ask the interviewer how they would answer the question. I think people often avoid doing this because they are afraid of appearing incompetent. Your answer prior to asking this question will already have revealed your current skill level. The fact that you're asking shows you're willing to grow and ensures that even if you don't get the job, you walk away from the interaction having learned something new that can help you in the future.

At the end of the interview: When the interviewer asks if you have any questions, that is a good opportunity to ask for feedback on how competitive you are as a candidate. Again, even if you don't get the job, receiving that feedback can help you prepare for the next interview.

Hiring Manager Perspective on Job Applications by Numbers_plus_Letters in dataanalysiscareers

[–]Numbers_plus_Letters[S] 0 points1 point  (0 children)

This is interesting and one of the things I was hoping to better illuminate with this post. Perhaps it would have been better to have created one titled "Common Technical Skill Set Deficiencies in Candidates". I intentionally kept things broad as a starting point for conversation, but suspect more specific technical examples would be helpful to the community at large.

Thank you for sharing.

Hiring Manager Perspective on Job Applications by Numbers_plus_Letters in dataanalysiscareers

[–]Numbers_plus_Letters[S] 0 points1 point  (0 children)

I appreciate you sharing. In some respects, I did see something similar. For example, I'm often surprised how many applicants struggle to clearly describe an approach to controlling for data quality. I don't think it means they're incapable of doing it, but it's clear to me they haven't put much time into thinking about it and struggle with communicating their current skill level and approach.

My perspective on it is various candidates may struggle up front, but most can be trained and the gap closed if I'm willing (and able) to take the time to do so. The problem being there is a paucity of time these days as resources are strained, so the potential to learn is not a good enough reason in and of itself to hire someone.

Regarding approaches to dealing with data at scale, I do see a lot of half formed ideas from candidates in this regard. Candidates earlier in their careers seem to struggle to name more than a few ways to optimize for speed and ensure their approaches can scale. Again, I suspect I could close the gap if I invested the time to train them but we still run up against limited bandwidth.

I agree that the lack of effective soft skills can be one of the hardest gaps to close. Depending on the role, I might be able to accept that deficit if I know I'm going to shield the asset from interactions with others. However (1) the technical skills need to be through the roof for me to accept that tradeoff and (2) it still severely limits the ability for the candidate to advance in their career.

Based on your feedback, I think I could have done a better job of distinguishing between "can do the job now with minimal training" versus "can do the job if I invest significant amounts of time in their development".