I applied to 230 Data science jobs during last 2 months and this is what I’ve found.

Summary

The author, a data analyst with 2 years of experience and self-taught data science skills, has applied to 230 data science jobs and shares insights on the necessity of diverse skills, niche specialization, the importance of higher education, and the prevalence of data engineering roles in the job market.

Abstract

The author has been actively applying for data science positions over the past two months, gaining valuable insights into the current job market for data science professionals. The observations made during this extensive job search reveal that roles in data science are becoming increasingly multifaceted, often requiring expertise in areas such as AI, full-stack development, and statistical programming in addition to traditional data science skills. The job market appears to favor candidates with deep knowledge in specific niches, such as insurance or NLP, and there is a notable preference for candidates with advanced degrees like a master's or Ph.D. in quantitative fields. Interestingly, the author has found that there are more job opportunities in data engineering and data analysis compared to data science, suggesting a higher demand for data engineers. The article concludes by inviting readers to share their thoughts and experiences, fostering a community of support and knowledge exchange for those navigating the data industry.

Opinions

Data science roles are evolving to require a broader set of skills beyond core data science knowledge.
Specializing in a niche area within data science can give job seekers an advantage in the job market.
Advanced degrees in quantitative fields are highly valued by employers in the data science sector.
There is a significant demand for data engineering roles, which may present more job opportunities than traditional data science positions.
Data analysts looking to transition into data science may find that roles in data engineering or data analysis offer similar compensation and promising career prospects.
The author encourages an open dialogue among professionals in the field to share insights and support each other's career growth.
The author recommends an AI service, ZAI.chat, as a cost-effective alternative to ChatGPT Plus (GPT-4), highlighting its affordability and performance.

A little bit about myself: I have been working as a Data Analyst for a little over 2 years. Additionally, for the past year, I have been teaching myself the concepts of data science while simultaneously working on my own project. Over the last year, I have acquired a substantial amount of knowledge and skills.

Although I am happy with my current role and compensation, which aligns with that of a Data Scientist, I remain interested in transitioning to a career in Data Science. For the past two months, I have been actively applying for Data Science positions, and during my job hunt, I have come across several noteworthy observations:

Disclaimer: The following observations are intended to share my personal insights and are not meant to demotivate anyone.

Data science alone is not sufficient: While I may not be fully aware of the past circumstances, I have observed a current trend where around 30% to 35% of jobs are oriented towards roles such as AI programmers, full-stack data scientists, and statistical programmers. These positions demand skills beyond traditional data science expertise.

source: LinkedIn

Most of the Jobs are niche specific: Many jobs in data science are focused on specific areas. For example, if you want to work with data in the insurance field, you might need to know about pricing models. On the other hand, if you’re looking to work in a call center, they might want you to understand NLP (Natural Language Processing) models. So, having deep knowledge in one particular area can really help you stand out.

Getting a higher qualification matters: I read articles last year about people entering data science jobs after attending boot camps or teaching themselves. I’m not sure if this still happens, but from what I’ve seen, around 40% of job ads ask for a master’s or Ph.D. in a quantitative field. And if not that, then about 90% of jobs prefer a bachelor’s degree in engineering, math, or physics.

source: Linkedin

Fewer job opportunities compared to other data-related roles: When you search for data science jobs on platforms like LinkedIn or Indeed, you’ll notice that there are more jobs related to data engineering and data analysis than there are for data science. This suggests a significant demand for data engineers.

source: Linkedin

For newcomers entering the data industry, it seems like pursuing roles as data engineers or data analysts might be a wise choice. Notably, these roles offer good prospects, and if you consider the salary aspect, they often receive similar compensation.

Please write me a comment about your opinion on this insight

Have you all noticed the same trends? Sharing our thoughts can be so beneficial because, in the end, we all want each other to succeed. Let’s continue this exchange of ideas to support one another on our journeys.

Thanks!

I’ve applied to 230 Data science jobs and this is what I’ve found.