What Hiring Manager Look from The Data Scientist Applicant

The insight from the experience of people who have do the hiring

Photo by Clem Onojeghuo on Unsplash

Let’s be honest a little. Most of us do the job for financial reasons. It’s fine for the passion, but having financial security can make a difference. That’s why people learn skills and make connections to get that dream job.

Data scientist is a job that people aim for as the demand is high, especially in the AI era. With higher demands, many companies are willing to pay high amounts to secure the best data scientists.

However, the supply of data scientist applicants has increased with the increase in positions. With too many applicants, people need to compete for the position. Especially for junior data scientist positions, as these entry positions are the key to future data careers.

With the hard challenge of entering the data science career, we need to improve our standing in the eyes of the hiring manager. After all, they are the ones who will decide if we can get the job or not.

In this newsletter, I will share my insight on what the hiring manager would like to see from your application as a data scientist and during the interviews.

Let’s get into it.

What Hiring Managers See in Your Data Science Resume Application

Let’s start with things the hiring manager usually does not care much about. For example, the hiring manager would not care about where you came from, birthplace, appearance, religion, or any discriminating profile. At least the real hiring manager, but bias could still happen, yet it shouldn’t be.

So, let’s see what the hiring manager wants in your resume or CV as a data scientist applicant.

The first thing the hiring manager would obviously see is the technical skills. Depending on the business needs, the technical skills could be varied, but data scientists should have the same basic things.

Technical Skill

For some, the technical skills should:

Programming

• Python: Proficiency in libraries like NumPy, Pandas, and Scikit-learn. OR

• R: Familiarity with tidyverse, ggplot2, caret

• Version control with Git

Statistical Analysis

• Description Statistic

• Inferential Statistic

• Experimental design

Machine Learning

• Supervised learning: Classification, regression

• Unsupervised learning: Clustering, dimensionality reduction

• Deep learning basics

• Model evaluation and validation techniques

Data Visualization

• Tools: Matplotlib, Seaborn, ggplot2, Tableau, PowerBI

• Ability to create clear, informative visualizations

SQL and Database Knowledge

• Understanding database design principles

• Simple Queries

But how do you show that you have the technical skills? It might be easy to write it down in the resume. However, it might not be enough proof.

So, we need to have a Project Portfolio. There are multiple ways where to put your data science portfolio, but these three are the most common ones:

• GitHub Repository: Create a well-organized GitHub profile.

• Personal Website: Showcase your projects in detail on the website.

• Blog: Write blog posts explaining your approach to data science problems.

Your Project Portfolio should be shown that you can work as a data scientist. Here are some tips to improve your portfolio:

• Diverse Projects: 3-5 projects showcasing different skills (e.g., classification, data visualization, NLP)

• Clear Structure: Each project should include:

  • Problem statement

  • Data description

  • Methodology

  • Code

  • Results and interpretation

  • Challenges and lessons learned

• Quality Over Quantity: Focus on your best work

Try to give the tips above some thought and work on them as best as possible.

Here are some example project Portfolio to inspire you:

• End-to-End Machine Learning Project Development: Spam Classifier

• Perceptions of discrimination in the US in 2014

• James’ Portfolio Website

In addition to technical skills, data scientists in the current era are expected to have soft skills that are necessary to succeed in the company and get noticed by the hiring manager.

Soft Skill

Other than technical skills, data scientists need soft skills. However, most junior data scientist applicants, especially those fresh from school, might not have these soft skills.

That’s why I would list some of the soft skills as something nice to have rather than what the hiring manager would want to see. Soft skills such as:

• Communication Skills

• Problem-Solving

• Collaboration and Teamwork

• Business Acumen

• Adaptability and Continuous Learning

• Time Management and Prioritization

• Critical Thinking

• Storytelling with Data

• Ethical Considerations

• Leadership and Initiative

If you feel you already possess these skills, go ahead and write them down within your resume. But you need to be responsible for what you have written. So, you need to demonstrate that you have these skills.

To demonstrate these skills, showcase specific examples in your resume, portfolio, and interviews where you've applied them. Highlight projects where you've communicated technical concepts to non-technical stakeholders, collaborated on cross-functional teams, solved complex problems creatively, or led data-driven initiatives.

Quantify the impact of your work where possible, and be prepared to discuss scenarios that demonstrate your soft skills.

Those are examples of how to demonstrate your soft skills. It’s usually ask much more during the interview process.

What Hiring Managers See in Your Data Science Interview

Congratulations! Your resume and CV stand out if you are invited for a data science interview. This is the time for you to shine and awe all the interviewers.

At this moment, the hiring managers would look for a combination of technical skills, problem-solving abilities, communication prowess, and cultural fit. They want to see that you can work with data effectively and translate that work into business value.

To prepare for these assessments:

1. Review your technical skills: Be ready to discuss or demonstrate your coding abilities, statistical knowledge, and familiarity with relevant tools and technologies.

2. Practice problem-solving: Prepare to explain your approach to solving complex data problems. Structure your responses using the STAR method (Situation, Task, Action, Result).

3. Enhance your communication: Practice explaining technical concepts in simple terms. Be prepared to present your past projects clearly and concisely.

4. Understand the business context: Research the company and industry to show how you can apply data science to their specific challenges.

5. Reflect on your projects: Be ready to discuss your past work in depth, including challenges faced and lessons learned.

6. Consider ethical implications: Consider how you approach data privacy, bias, and responsible AI use.

7. Showcase your data intuition: Prepare examples of how you've identified patterns or issues in data that others might have missed.

8. Prepare questions: Show your interest and initiative by asking thoughtful questions about the role and the company's data science initiatives.

Remember that the interview process is about showcasing your current work and highlighting your potential to grow with the company. Be honest about your abilities, show enthusiasm for the field, and don't be afraid to discuss areas where you're still learning.

Prepare both your resume and interview skills to improve your chances of being hired as a data scientist.

That is all for now.

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If you need any help or want me to write something about your interest, just comment or contact me on my social networks. Or even better, use the chat!

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Articles to Read

Here are some of my latest articles you might miss this week.

Photo by Patrick Tomasso on Unsplash

• How to Simplify Data with Dimensionality Reduction Techniques in Scikit-learn in Statology.

• How to Simplify Data with Dimensionality Reduction Techniques in Scikit-learn in Statology.

• How to Use Scikit-learn’s RandomizedSearchCV for Efficient Hyperparameter Tuning in Statology.

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Announcement to Make

I am starting to write an e-book about practical end-to-end data science projects focusing on practicality and business values. I would include use cases from different parts, including:

• Classification

• Regression

• Unsupervised Learning

• Time-Series

• LLM

There are many more to update. Subscribe to my newsletter and keep up with my posts to stay informed.

The working cover is below, but it is not final yet.🤗