Beyond the Resume: 5 Keys to Becoming an In-Demand Data Scientist

Data Scientist Game Plan

9/20/20233 min read

The journey to becoming an attractive Data Scientist candidate has been a rollercoaster ride filled with highs and lows. Today, I read an article by Jeremie Harris, Co-founder of Gladstone AI, called โ€œTo get hired as a data scientist, donโ€™t follow the herdโ€, and as someone who's still working to overcome the hurdles, Iโ€™m writing this article in the hope that it resonates with others facing similar challenges.

First, let's find what we have in common:

1. A Strong Educational Foundation: ๐Ÿ’Ž

I've laid a solid educational foundation with an MBA specializing in Data Analytics and a perfect 4.0 GPA. While this accomplishment fills me with pride, I've come to realize that education, while crucial, is just the first step in a long journey.

2. Experience as an HR Data Analyst: ๐Ÿ’Ž

My six years of service in the US Army as an HR Data Analyst provided me with valuable analytical skills. Yet, translating this unique #military experience into civilian terms and job applications remains a formidable challenge.

3. Leveraging My Location: ๐Ÿ’Ž

I'm fortunate to be located near Austin, TX, a burgeoning tech hub. However, the competition is fierce, and I often wonder if I'm doing enough to connect with the local tech community.

4. Certifications Matter: ๐Ÿ’Ž

I've invested time and effort into gaining certifications through the IBM Data Analyst Professional Certificate, but the job market remains elusive. Certifications alone don't guarantee opportunities, and I'm grappling with how to stand out from the crowd.

5. The Elusive Callback: ๐Ÿ˜ซ

Despite my qualifications and aspirations, the harsh reality is that I've struggled to secure callbacks for job applications. It's disheartening, and at times, it feels like an insurmountable obstacle. I've asked myself many times, "What am I missing?"

Sounds familiar?

Facing the Challenge Head-On:

While the journey hasn't been smooth, I'm committed to facing these challenges head-on. Here are some action items I am focusing on this month based on Harris' 5 recommendations:

1. Replicate Papers:

  • Action Item 1: Select a recent and interesting paper about Genomic Data Visualization.

  • Action Item 2: Attempt to replicate the paper's results using a new dataset or by making modifications.

2. Avoid Comfort Zones:

  • Action Item 1: Identify repetitive tasks or tools in your current projects.

  • Action Item 2: Set a goal to learn a new framework, library, or tool to diversify your skills.

3. Learn Boring Things:

  • Action Item 1: Invest time in learning version control systems like Git and Git workflows.

  • Action Item 2: Gain experience in deploying models on cloud platforms like AWS or Google Cloud.

4. Do Annoying Things:

  • Action Item 1: Attend local data science meetups or events in your area to network. Any recommendations for #Texans?

  • Action Item 2: Offer to present a paper on the Genomic Data Visualization at a local meetup to collaborate.

5. Do Things That Seem Crazy:

  • Action Item 1: Identify a unique and challenging project idea that involves creating a custom dataset.

  • Action Item 2: Learn web scraping techniques or leverage underutilized APIs to collect data.


I'm deeply passionate about using genetic data to uncover the migration histories of ancient populations and the potential to investigate how this genetic information can reveal connections between individuals today, whose ancestral backgrounds may have experienced disruptions, and the inheritance of diseases and unique physical traits within these groups.


Let's Remember:

"...if you want an outstanding outcome, you have to do outstanding things." -Jeremie Harris

Closing Thoughts:

As someone who's still navigating the challenges on the path to becoming an attractive Data Scientist candidate, I want to convey that you're not alone if you're facing similar hurdles. It's okay to have doubts and setbacks; they're part of the journey. What's important is the resilience to keep moving forward, the determination to learn and adapt, and the belief that your unique background and skills have value in the world of data science.

#DataScience #MachineLearning #DataAnalytics #DataScientists #AIandML #Bioinformatics #Genomics #BioData #Sequencing #ComputationalBiology

If you're on a similar journey, please reach out, share your experiences, or offer insights. Let's support each other as we navigate this challenging but ultimately rewarding path.