Who gets hired? An outlook of the U.S. Data Scientist Job Market in 2018.
Take-away Message for future data scientists:
- Read job descriptions carefully.
- Top 5 tools : Python, SQL, R, Excel and Scala.
- Top 5 skills: Machine Learning, Modeling, Optimization, Data Visualization, Artificial Intelligence.
- Most desired degree: Master and PhD.
- Most desired major: Data Science, Statistics, Mathematics and majors related to quantitative fields.
For those who are actively looking for data scientist jobs in the U.S., the best news this month is the LinkedIn Workforce Report August 2018. According to the report, there is a shortage of 151,717 people with data science skills, with particularly acute shortages in New York City, San Francisco Bay Area and Los Angeles.
To help job hunters (including me) to better understand the job market, I scraped Indeed website and collected information of 7,000 data scientist jobs around the U.S. on August 3rd. The information that I collected are: Company Name, Position Name, Location, Job Description, and Number of Reviews of the Company (Download the dataset from Kaggle).
What Tools and Skills are desired the most?
Excel, Python and SQL turn out to be the most desired tools for data scientists where machine learning, modeling, and optimization are key skills that are mentioned the most in job descriptions. Counter intuitively, excel turns out to be the most desired tool that employers want. It has been mentioned in 83% of the job descriptions.
Does degree matter?
Computer Science is still the most popular major, followed by quantitative majors including Statistics, Mathematics and Economics. Surprisingly, Data Science major has only been mentioned 4 times out of 100 job descriptions on average. This might be because employers are aware of the shortage of the data science programs in the U.S.
Do employers know what type of talents they want?
Quick answer is, not really : (
Have you gone through interviews where you are ready to be asked about Logistic Regression and Random Forest but end up whiteboard coding an algorithm problem? You'll never know if they want a data scientist or a software engineer. Exhausting.
To address this concern, I separated all the positions into three groups: engineer, data scientist and data analyst. By comparing the job descriptions of these three groups, I would like to know if they show distinct features in job functions, degree requirement and major preference.
Engineer vs. Data Scientist vs. Data Analyst Tools
This graph compares the tool frequency between these three groups. The number on each bar is the rank of the tool frequency within each group, the length of each bar represents how often this tool has been mentioned in job description for each group. The longer the bar, the more desired is this tool. For example, for data scientists, the top popular tools are python, SQL, R, Excel and Scala where programming languages are more important for engineers. Data scientists and analysts don't show significant difference in terms of the tools they use.
Engineer vs. Data Scientist vs. Data Analyst Skills
For skills, engineer only show preference for artificial intelligence. Data scientists show preference for skills including machine learning, modeling and statistical analysis where analyst show preference for data analysis, data visualization and research.
Engineer vs. Data Scientist vs. Data Analyst Degrees and Majors
In general, data scientists require a higher degree than engineer and analyst positions on average. For major preference, engineer positions show a significant preference for computer science majors where data scientists and analyst don't have a strict major requirement. Quantitative majors are generally acceptable to data scientists and analysts.
From my analysis, job postings with the title "data scientists" and "analysts" have a high level overlap concerning desired tools, skills, degrees and majors. For job hunters, it is very important for you to read the job description carefully and make judgments.
Back to our question, who gets hired?
If you have a master's degree (or higher) in a quantitative field, proficient in Python, SQL, Excel, R and Scala, mastered machine learning, modeling, optimization, data visualization and artificial intelligence, and most importantly, you read job descriptions carefully, you'll be hired!
(See my codes on Github; Link to the PowerPoint)