For those of you who read my last blog, I looked at how the data science job market had performed in 2023 – at least since August when the data collection began. The end of the calendar year provided a fitting moment to take a step back and assess what had transpired in the ensuing months so that we could take note of any developments and trends which might extend into the new year.
What we saw was a vibrant job market in Toronto as it emerged as a significant, if not leading hub for data science related jobs. We also began to witness the rise, or increased demand for Machine Learning Engineering positions – something I had predicted in my August blog. (I never lose an opportunity to be self congratulatory.) We also tracked the fluctuations in the number of job postings from a month to month basis, while determining a consistency in salary and the major market players both in Toronto and in North America.
Given all that, what will the new year bring? Will it largely be a continuation of the previous year, or would 2024 mark a significant deviation from past trends? Let’s find out!
January’s Job Postings: A Promising Start to the New Year
In the previous blog I looked at the overall number of job postings and how those numbers fluctuated from month to month. If you recall, the month of August yielded the greatest amount of job postings, before declining the following month and somewhat stabilizing in a particular range, before nose diving in December.
If you also recall, I urged a fair bit of caution before reading too much into the December dip, given that December is not a hiring month and that we would have to wait for the January numbers to see that this downward trajectory was only momentary. And I was right again. (Did I mention I never lose an opportunity to be self congratulatory?)
January was actually the second most successful month in terms of job postings:
- There were 7352 job postings, second only to August’s total of 7515
- There was a 65% increase in the number of postings when compared to December
And we can see that his overall trend in data science related postings matched the four main positions we have been tracking – Data Analyst, Data Scientist, Data Engineer and Machine Learning Engineer – with each posting enjoying an increase in demand for this month.
This is certainly wonderful news and a great way to bring in the new year, however I would still exercise a level of caution with these numbers. I suspect that to some extent January’s numbers are inflated by the number of postings that were put off because of the holiday season that found its way into January’s output.
The Unstoppable Ascent of Machine Learning Engineers
(The above was almost a clever subtitle). But while we’re looking at the job numbers for the four positions listed above, it is definitely worth noting that machine learning engineering postings continue to increase, thus continuing a 2023 trend.
You can see from the chart below that the percentage of machine learning engineer postings has steadily increased relative to the other three data science positions. Continuing a self congratulatory lap, I predicted this as well.
I would also like to remind readers that up until a couple of months ago, this particular position always had the fewest number of job postings, before it surpassed data engineering. And now, it is almost on par with data science jobs.
So, why the continued increase? Well, I would like to offer up the following reasons:
Advancements in Technology: The rapid advancements in machine learning and artificial intelligence technologies have created a need for skilled professionals who can develop, implement, and maintain these systems.
Data Explosion: The exponential growth of data in various industries has led to an increased demand for machine learning engineers who can analyze and extract valuable insights from large datasets.
Business Applications: Companies across different sectors are recognizing the potential of machine learning to improve decision-making, automate processes, enhance customer experiences, and drive innovation. This has led to a surge in demand for ML engineers to implement these solutions.
Competitive Advantage: Organizations are seeking to gain a competitive edge by leveraging machine learning algorithms to optimize operations, reduce costs, and create new products and services.
Research and Development: There is a growing investment in research and development of new machine learning algorithms and techniques, further driving the demand for skilled ML engineers.
Overall, the increasing demand for machine learning engineering positions is driven by the growing importance of data-driven decision-making and the continuous advancements in AI and machine learning technologies. And later on, we’ll see the big tech companies continuing to prioritize this position.
Having looked at the job numbers, let’s turn our attention to where the bulk of these numbers come from.
Toronto’s Reign and New York’s Resurgence
When we last left 2023, Toronto had pretty much become the pre-eminent job provider. Ever since October, Toronto has outputted very strong job numbers relative to the other top ten cities. It seems it’s string of first place finishes for monthly postings we interrupted by Seattle and the month of December. Given that I take December to be an irregular month, we can see that Toronto has continued where it left off, once again taking top honors this month.
Still, just to give you an indication of how close some of these totals are from month to month we can look at the graph below.
What also may be of note is the possible re-emergence of New York, the previous job postings champ. You can see from the above graph that its numbers had been declining since August. However, January provided an uptick in the number of job postings and it just narrowly lost out to Toronto for the top spot this month. Is this just a good month, or a sign of things to come? I guess we’ll have to stay tuned.
Diverse Leadership: A Breakdown of Job Types Across Top Cities
It’s always interesting to take a look at the top 10 cities and see which of them are leading the charge in each of the different job types. Looking at the data, here are my observations for each job type
Data Analyst Positions:
- Continuing the trend it had established in 2023 as the largest poster of data analyst positions, Toronto finished first four the fourth consecutive month
- Washington has also been fairly consistent in that time, finishing second for the fourth time in six months.
- After a continued decline until November, New York might be back into the swing of things. Maybe?
- Beyond that, there’s not that much consistency among the other top 10 cities.
Data Science Positions
- Where Toronto was riding high with Data Analyst postings, this has not been the case with data science, having gradually fallen, relative to the other major cities, from an October high when it led the way in data science postings, having slipped a position in each succeeding month.
- In the course of one month, New York went from fifth to first!
- San Francisco seems to have traded places with New York going first first to fifth!
- Atlanta was also a big climber going from eight to third.
Data Engineering Positions
- It looks like Washington is the place to be for data engineering positions, holding the top spot for three straight months.
- New York has also come in second for two straight months
- Much like it did for data science positions, Atlanta made a big jump in the rankings, this time going from seventh to third.
Machine Learning Engineering Positions
- Most strikingly, San Francisco has seen its MLE dominance decline.
- It has continually ranked first in three consecutive months (August to October) and has declined in every succeeding month, finishing in fifth this month.
- Toronto tops the MLE job postings for the first time
- New York finds itself at fourth from a low of ten in November
- San Jose also enjoyed a big jump going from tenth just last month to sixth this month
We’ll have to follow up with these cities in the months to come, but it seems like New York, in general, has taken an upswing. And while Toronto has seen its data science postings decrease, its MLE postings have increased interestingly enough.
Show Me the Money
Of the top 10 cities, which are the most lucrative to be employed in? This is something I’ve charted from the beginning, and from the beginning we have had but one city name at the top of the list: San Franciscio. And this month is no different.
San Francisco leads the way with an average salary of $141 000, followed by its Silicon Valley counterpart Cupertino at $130 000 and New York rounding up the top 3 at $116 000.
As you look at the ups and downs of each city in each month, I would, once again, urge caution. Given that the number of job postings for each city provides a relatively small sample size, and that this sample size is reduced further by the number of postings that actually post salary numbers, these numbers on a monthly basis might not be the most reliable.
With that said, let’s just refer to the North American average for this month because we have a much larger sample size. If we do that we see the following salaries for each job type:
- Data Analyst: $73056
- Data Engineer: $105963
- Data Scientist: $110365
- Machine Learning Engineer: $122104
If we compare that to the salary information that has been collected over six months we can see that, with the exception of data engineering, January’s numbers are a touch lower:
- Data Analyst: $75426
- Data Engineer: $105204
- Data Scientist: $113339
- Machine Learning Engineer: $127989
In any event, we’ll have to see how February’s numbers fare.
2024’s Biggest Hiring Companies…So far
While it is only January, this year’s list looks a lot like the previous years. What does that mean? Well, big tech is definitely present with 4 out of the top 10 companies on the list (Apple, Amazon, Google, Microsoft). Booz Allen Hamilton, a defense arm of the US government, continues where it left off last year, with the second most amount of postings after Apple. And then you have your banks/financial services (Deloitte and Capital One), your health related services (CVS Health, HCA Healthcare) and Walmart.
You can also see how heavily Big Tech is investing in machine learning positions with Google almost exclusively posting machine learning engineering positions.
Conclusion: Embracing the Dynamic Landscape of Data Science Jobs
As we wrap up our analysis of the data science job market’s journey from 2023 into 2024, it’s clear that the field continues to be a vibrant and ever-evolving landscape. Toronto’s ascendancy as a major hub and the rise of Machine Learning Engineer roles are just a few of the highlights that underscore the dynamic nature of this industry.
The resurgence in job postings in January, particularly in Toronto and potentially New York, signals a promising start to the new year. Yet, as always, a pinch of caution is warranted, especially when interpreting monthly fluctuations and salary data, which can be influenced by various factors.
The increasing demand for Machine Learning Engineers is a testament to the relentless march of technology and the growing importance of data-driven decision-making. As big tech companies continue to prioritize these roles, we can expect this trend to persist, shaping the job market in the months and years to come.
The shifting tides in the top cities for data science jobs, with Toronto leading the charge and New York making a potential comeback, add an intriguing layer to the narrative. The ebb and flow of job postings across different cities and job types highlight the fluidity of the market and the diverse opportunities available for data science professionals.
As we move forward into 2024, the data science job market promises to remain a dynamic and exciting field, full of opportunities and challenges. Whether you’re a seasoned data scientist or a newcomer to the field, staying informed and adaptable will be key to navigating the shifts and trends in this ever-changing landscape. Here’s to a year of growth, discovery, and continued self-congratulatory moments (because why not?) in the world of data science!
