Analysing My Search For A Lead Data Scientist Job
I studied Maths at University and spent the next 3.5 years working at Virgin Media. I initially joined as a Data Analyst and eventually transitioned in to a Data Scientist. After spending a few years developing machine learning models and performing data exploitation, I decided that it was time to explore other opportunities and work with some new data.
Due to the current exponential growth of data, many companies are looking to gain insight by hiring Data Scientists. Often many of these companies don’t have the infrastructure or senior support needed to truly benefit from data science. There is also currently a shortage of Data Scientists, especially those with a mathematical understanding of the algorithms used. As a result of this, experienced Data Scientists are in high demand and many recruiters are eager to discuss potential opportunities.
During the job searching process I had quite specific requirements and wanted to ensure that the company I worked for was able to implement data science effectively or already had an analytics team. I also wanted to ensure that the job description matched my expectation; the terms Data Scientist, Data Engineer & Data Analyst are often used interchangeably and I specialise in analytics rather than governance.
Analysis & Insight
With the context out of the way, let’s move on to the analysis.
I made a spreadsheet containing every job I was offered or applied to. I recorded the outcome, the salary, the company name and the industry. For reference I live in the UK and all figures are in pounds.
As you can see, changing my LinkedIn career status to “Actively Applying” drastically increased the number of views my profile was getting. I had my status as “Actively Applying” from mid July for 6 weeks, at which point I secured a role and changed my status to “Not Open To Offers.” Doing so instantly reduced the number of views, interestingly however, it spiked again a week later when I added my new job; I assume this was due to curiosity.
The majority of the roles I considered came from recruiters contacting me on LinkedIn, although this can become tiresome it’s an extremely useful platform for Data Scientists looking for work and I highly recommend using it. Although the sample size was significantly smaller, in the 4 instances where I applied directly, none of them had a positive outcome. Of the 71 roles considered, I rejected 51, I was rejected by 4, there was no response from 15 and I finally accepted 1.
The mean salary offered was £93k although the distribution was quite wide with the majority of roles falling in the 25th to 75th percentile: £87.7k to £100.3k, the lowest Senior Data Scientist salary offered was £66k and the highest was £122k.
Generally, Consultancy and FinTech paid the highest, although Consultancy had a high amount of variance, Retail also had a high amount of variance although the mean pay was over £20k less. Some roles are missing salary information and hence have a small sample size.
In terms of the number of roles, FinTech was the most popular industry with 14 roles. Consultancy was also popular with 12 roles.
Overall, the job search took almost two months, due to the complexity of the role, it’s worth spending the time finding the right position for you. I hope this helps others who are looking for a Lead Data Scientist role or are considering a career in Data Science.
If you have any questions, feel free to ask.
Most of the visualisation was done within R & Rstudio although the snakey diagram was done within python.
The data was gathered manually by recording data from LinkedIn, recruiters emails and interviews on Excel.
In general based on my experience for lead level roles, most companies were looking for someone with management experience who has led data science teams and projects previously.
The most highly desired skills were Python, R, SQL, AWS, Hadoop, Spark; roughly in that order.