In this story, I would like to share my recent conversation with about 10 enthusiastic young data scientists and what I learned from them in terms of job hunting tips and self reflection on my journey toward data science.
I had recently organized a panel discussion with two panelists with senior roles in data analytics and 5 newly minted data scientists from schools and boot camps. After that, there were a few more job seekers reached out to me for advice. I noticed a common theme in their questions and felt it would benefit more people by sharing what I have learned from them and the panelists.
In particular, I would like to touch upon the following topics:
Tips for Effective Networking on LinkedIn
Resume and Fit to Job Posting
Tips for Effective Networking on LinkedIn
There are quite a few great articles about how to portrait a great image on your Linked, so I would not touch upon that. Instead, I would like to touch upon importance of aligning your Profile and Etiquette.
Let me begin with an example — Jack (alias) first reached out to me on LinkedIn and would like to get my advice in data analytics. I accepted the invitation as he was referred by a communal friend. After that, I was expecting him to propose a time for a call along with some questions. However, nothing happened within the first week. In most cases, Jack’s networking story would stop here, and he would feel discouraged.
Networking Tip #1. Be Proactive.
Reason: People get a lot of LinkedIn requests everyday and often prioritize their time to get back to those most likely to succeed. Usually these would be the proactive ones.
I was once in Jack’s shoes, we have a communal friend, and Jack has a really good track record in data analytics. I have to admit that Jack’s LinkedIn profile, work portfolio on GitHub and Tableau Public were all too impressive to ignore. Hence, I reached out to him, offered my help and arranged a call with him as I happened to have some questions for him as well.
Networking Tip #2. Have an Impressive LinkedIn profile.
Reason: People had been in your shoes before, and understand that you might just be patiently waiting for responses and giving us time. By seeing your impressive profile, the networking becomes two-way street — the other person might be interested in learning from your story and sharing his/hers in return.
As it turned out, Jack had been applying to Data Scientist roles without much luck because he had great work experience in data analytics, and data science seemed a natural next step to him. However, I found that he had relatively less exposure to data science compared to data analytics so I suggested he applied for managerial roles in data analytics. He did, got an interview within a week, and an superb offer in a second week.
Networking Tips #3. Be Open to Ideas and Suggestions.
Reason: if you don’t, you are missing out a lot of great opportunities out there. You reach out to others for their advice so that you can see past your blind spots. If you are not open to ideas, then you would likely stay where you are for a very long time.
Resume and Fit to Job Posting
Recently, about 10 people reached out to me for one particular entry-level data scientist role they all came across. It’s likely just a small fraction of the total applicants. Let’s assume this represents 10% of the total, and it would mean that each of them are competing among 99 other strong candidates. Quite competitive with this conservative estimate. If the fraction represents 1% of the application cohort, then that’s 1,000 applicants for 1 single entry-level role. Well, you get the idea.
I would like to touch upon three people that stand out to me and the main reason being their good fit to the job posting. Long story short, there are three main requirements for this role, the main part being the usual data science job description (requirement #1), a second part more commonly for data analytics roles (requirement #2), and a third part you rarely see in data scientist roles (requirement #3).
One person stood out because he has touched upon requirement #3. In contrast, most others overly focused on requirement #1, so their resumes look very generic to me and I cannot really tell them part. A lot of them actually looked like a list of Python libraries you need to use for machine learning.
Resume Tip #1. Stand Out by Addressing Key Job Requirements
Reasons: most people don’t tailor their resume. Likely they would have one for all data scientist jobs regardless of the requirements. This would become very obvious when you are only addressing part of the job requirements. Those other requirements (#2, #3 in this example) are there for a reason. You would have a better chance by presenting your relevant experience, or acknowledge that you don’t but have a plan to fill that gap. Ignoring these seemingly minor, side requirements would suggest that you either don’t care about this job or you don’t know how to communicate what you cannot do and how to improve on it.
The second person stood out because he actually pointed out that the job description is unusual for data science role — “it seemed to cover jobs for an analyst, a data engineer, and a data scientist.” He has a strong resume that already addresses requirement#1 and #2. What caught my attention was his plan to develop skills for requirement#3. His plan was to look into the Python libraries and GitHub examples for it, build his own portfolio, and add a new line to his resume. He would then make proper update on his resume — that he looked into this requirement for this particular job and either that he now feels comfortable about it, or didn’t get it to work but is interested in learning more about it after he joins the team.
Resume Tip #2. Stand Out by Looking into the Missing Skills
Reasons: at minimal, people would know that you care, and are willing to look into it before anything is firm, such curiosity-driven learning is very important for data scientists who often have to pick up new skills on the job. We would like to know you’re willing to learn new things. In addition, if you actually pick up this new skill, it would be a great proof to the prior point. Sometimes these unusual requirements are there because the team wants to explore something — perhaps no one on the team actually knows how to do it. Having something to talk about is better than nothing, right?
The last person’s skillset was relatively common so it only addressed the usual data science requirement (requirement #1). However, his resume stood out to me because he gave great examples of the work he had done. These are mostly course work from a boot camp, but he added context to his machine learning projects, as well as how this would contribute to the team once he joined. It was very clear that he did his research about the company in general. His understanding was not entirely correct, but it was okay — there was just that much one can learn from outside the company. This sent a clear message that he cared and was willing to invest time and efforts. Usually I would give him the benefit of doubt for the two missing skill sets. He stood out because his resume and experience now seemed very relevant.
Resume Tip #3. Stand Out by Thinking from Business Perspectives
Reasons: often time, data scientists are hired to address pressing business problems. It is therefore very important that you show your interests, understanding, and skills in solving these business problems. In addition, you would make a good connection by researching the company and relate your experience to it. That would foster a mindset that you are already one of us, and we are already talking about what to do next.
In conclusion, it is a lot of fun to be a data scientist, but you would need to invest your time and efforts in effective networking and job application before you become one. It is a long journey that I am taking myself, and I hope my story would make your journey more pleasant.
I just begin writing very recently, so there would be a lot for me to learn. I thought it would be very helpful to share with you a great article I came across a great article over the weekend. Check that out.