Data science roles are increasing as companies realize the importance of data scientists in their strategic operations. A company with a team of data scientists better sees numbers and makes sense of data for growth and competitive advantage. Some companies do well without the positions of data scientists and analysts but often face roadblocks in crunching the numbers.  

Glassdoor estimates more hiring of data scientists in 2021 and demand will increase as digital transformation takes shape. Data science¹ is the coolest job based on recent polls conducted by Gartner and CB Insights and as we begin the second quarter of 2021, this trend will continue.

Recruiting data science teams requires an understanding of factors including education, technical skills, projects and business outcomes of candidates. Zip Recruiter and Indeed.com are some popular websites with many applications around data science roles including machine learning engineers and developers.

Hiring new data science teams means exploring the curriculum vitae of every candidate and looking for specific attributes needed by the company. Some recruiters make mistakes in the hiring process and this becomes costly for them in the long haul. The recruiter goes through the CV of the data scientist by scanning through and checking what makes the candidate stand out.

For data scientists² looking for new roles, everything comes down to the CV. A good CV can make or break your chances of being hired and this is where most candidates should focus on to improve their chances. In this article, I will explore some key areas candidates in data science roles should check out to make sure, their CV rocks.   

1. Business Results

Data scientists lead companies in analyzing data and reporting to the C-Suite³ about the results and implications to the business. A good data scientist must prove understanding of technical areas of the business and explain how their involvement created outcomes for the business. A company must look at return on investment and the same applies to hiring data scientists. No one needs a data scientist incapable of using numbers to predict and impact the bottom line of the business.  

A good CV should assure the recruiter about your problem solving capabilities and how you will enable the business achieve return on investment and growth. Business results⁴ is also about risk analysis and the data scientist must mention about their projects and ideas that led to good business outcomes. I know many data scientists face this challenge and avoiding this problem means getting involved in the practical projects around data science to prepare you for the real world enterprise.  

2. Undertaken Projects

Recruiters and employers understand the importance of practical applications in data science and nothing says this than your GitHub⁵ account where people can see your code and understand your skills in depth. For example, as a recruiter, I would want to know your understanding of algorithms and feature engineering based on practical problem situations. GitHub is a good place to speak directly with recruiters about your code work and experience.  

Kaggle is another great platform you can use to display your involvement in data science projects and show your readiness to tackle any problems presented to you. Data scientists are trend spotters who find solutions to problems through detailed analysis. They use platforms like Kaggle for their code work and this is a great way of impressing employers. Participating in Kaggle⁶ based activities is a popular tool candidates use to handle data science interviews.  

3. Courses and Education

Your education speaks volumes about your experience as a data scientist and this section of the CV should outline your academic achievements. Data scientists should list all training institutions they attended as this tells the employer about your qualification for the job. Some candidates leave out details such as awards and these matters too in presenting a good CV.

Note that data science unlike most fields has people from different fields who join the career. For example, a student of statistics, mathematics and actuarial science can join the data science career despite not having undertaken a data science degree⁷. There is no standard model for measuring the training of data scientists because of their diverse experiences. So you should not worry about not having a degree in data science.  

Mention if you earned a degree in data science or another degree in another field. At the same time, some data scientists get training from boot camps and should include this information in their CV. Small details such as professional certifications and earning a place in the Dean’s list are good methods of showing your academic qualifications for the job.

4. Your Tech Stack

The recruiter wants to understand your experience with languages used in data science and this requires listing all your options. For example, Java, Python, and mlr are programming languages commonly used by data scientists and these assure employers about your qualifications. You should also mention tools such as XGBoost and RNN for a good rapport with the recruiter.

List your understanding of packages including Scikit and Pandas with details about your understanding of machine learning and using algorithms in model development⁸. The recruiter explores these areas to enable them get a clear picture of your capabilities and areas they should position you in their company. Companies have different settings of how their data science teams operate and doing some, research on popular packages and tools is a sure way of improving your chances of being hired.  

5. Data Science Experience

Your CV should list your experience working as a data scientist and different projects undertook during this time. The employer wants to know your level of data science experience and the best way is naming organizations and companies. Candidates sometimes leave data science experiences and this limits them from the perspective of the employer.

Companies have positions for data science to fill with others namely machine learning engineer, data engineer, and data analysts but requirements differ. For example, the position of a data scientist requires more experience and understanding of technical subjects such as programming languages. A data analyst does not have experience in programming languages such as Python compared to the data scientist.

Speak Directly to the Recruiter

There are other considerations data scientists should consider in their job hunt including understanding of machine learning domains and algorithms. Recruiters want to know your experience in machine learning and algorithms in data science for better productivity. Companies hire data scientists to make sense of data and use these numbers to add value.

Your role as a data scientist is to speak to your employer through your CV. It is important to consider other best practices for good CV’s such as a good design of the resume. The organization of your resume tells the employer about your readiness to handle your tasks and increase returns for the business. Remember to create a good first impression while being honest about your experiences.

Works Cited

¹Data Science, ²Data Scientists, ³C-Suite, ⁴Business Results, ⁵GitHub, ⁶Kaggle, ⁷Data Science Degree, ⁸Algorithms in Model Development