The headlines surrounding tech layoffs have been stark: since the start of the year, 421 tech companies have laid off 119,593 staff globally. In the UK, high rates of venture capital funding that fuelled hiring growth during the pandemic have slowed significantly, dropping by 22% in 2022. In turn, this has had a knock-on impact across hiring in many sectors, including the neobanking sector. So what does this mean for tech workers in the UK? While no sector is immune to redundancy, certain jobs are proving more resilient than others.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Let me start this blog by clarifying that I do not consider myself a data scientist nor a technical expert, but I have gained a pragmatic perspective on the various roles in this space through my experiences in leading AI & data science projects and building up and managing teams of data scientists and analytics professionals.

Ten years ago, the authors posited that being a data scientist was the “sexiest job of the 21st century.” A decade later, does the claim stand up? The job has grown in popularity and is generally well-paid, and the field is projected to experience more growth than almost any other by 2029. But the job has changed, in both large and small ways. It’s become better institutionalized, the scope of the job has been redefined, the technology it relies on has made huge strides, and the importance of non-technical expertise, such as ethics and change management, has grown. How it operates in companies — and how executives need to think about managing data science efforts — has changed, too, as businesses now need to create and oversee diverse data science teams rather than searching for data scientist unicorns. Finally, companies need to think about what comes next, and how they can begin to think about democratizing data science.

When looking at data scientist salaries and data science roles, it became obvious that there are different, more specific facets within data science. These facets relate to unique job positions, specifically, machine learning operations, NLP, data engineering, and data science itself. Of course, there are even more specific positions than these, but these can give you a general summary of what to expect if you land a job in one of these positions. I wanted to pick these four roles, too, because they can be separated well, almost as if it was there was a clustering algorithm that found jobs that were the most different between one another but that were also in the same population. Below, I will be discussing the average base pay with a low and high range, as well as respective seniority levels, the number of estimates used to determine these numbers, and expected skills and experiences for each role.

Hearing "No" at a job interview can be quite a demotivating experience. Applying for a data science job involves a lot of stages, while the interview stage can be crucial to gauge if an applicant will fit the job role and the company culture. Often, applicants get rejected for mistakes they commit that can easily be avoided. There is a huge hype around using words like deep learning, machine learning, and neural networks in a CV while applying for data science roles. An applicant should mention such skills in their CVs or during the interview only if one has worked on real-life projects previously in a job or internship.

Would you recommend a masters in Data Science (as offered by most Universities) or a masters in some specialised field? Kindly do advise from the point of view of job opportunities in the respective domains as well. I would recommend getting an MSc in Data Science if you are confident that you are only interested in employment opportunities within Data Science roles. There still seems to be a high demand for Data Scientists despite the current pandemic and the difficulties some industries are experiencing. At the same time, there is also an increased supply of data scientists, which translates to more competitions for roles.

Data Science is a gift to the modern world. The technology complements the existing data sources by making use of them. Recently, data science is being widely adopted by organizations to make predictive decisions on their behalf. Data science is a blend of various tools, algorithms and machine learning principles with the goal to discover hidden partners from raw data. The technology is primarily used to make decisions and predictions making use of predictive casual analytics, prescriptive analytics and machine learning.

To succeed in a machine learning and data science career, there are a lot of different elements you have to know quite well to be effective at your job. In this post, we'll go over the top 3 skills you should master as a data scientist! Data scientists are like engineers, but instead of coding a web app as a frontend engineer would do, they are responsible for architecting data processing pipelines, designing and implementing models, and developing infrastructure for system evaluation and metrics computation. As you can imagine, performing these tasks requires a reasonable amount of fluency with a high-level programming language (think Python, R, Matlab, or Julia), as well as data science specific libraries (think Pandas, Scikit-learn, Matplotlib, or Tensorflow). Developing this skill alone is something that can make up a year or more of an undergraduate computer science degree.

As we come to the end of 2019, we reflect on a year whose start already saw 100 machine learning papers published a day and its end looks to see a record-breaking funding year for AI. But the path getting real value from data science and AI can be a long and difficult journey. To paraphrase Eric Beinhocker from the Institute for New Economic Thinking, there are physical technologies that evolve at the pace of science, and social technologies that evolve at the pace at which humans can change -- much slower. Applied to the domain of data science and AI, the most sophisticated deep learning algorithms or the most robust and scalable real-time streaming data pipelines ('physical technology') mean little if decisions are not effectively made, organizational processes actively hinder data science and AI, and AI applications are not adopted due to lack of trust ('social technology'). With that in mind, my predictions for 2020 attempt to balance both aspects, with an emphasis on real value for companies, and not just'cool things' for data science teams.

As we come to the end of 2019, we reflect on a year whose start already saw 100 machine learning papers published a day and its end looks to see a record-breaking funding year for AI. But the path getting real value from data science and AI can be a long and difficult journey. To paraphrase Eric Beinhocker from the Institute for New Economic Thinking, there are physical technologies that evolve at the pace of science, and social technologies that evolve at the pace at which humans can change -- much slower. Applied to the domain of data science and AI, the most sophisticated deep learning algorithms or the most robust and scalable real-time streaming data pipelines ('physical technology') mean little if decisions are not effectively made, organizational processes actively hinder data science and AI, and AI applications are not adopted due to lack of trust ('social technology'). With that in mind, my predictions for 2020 attempt to balance both aspects, with an emphasis on real value for companies, and not just'cool things' for data science teams.