Artificial Intelligence (AI) is revolutionizing the workplace and creating new opportunities for high-paying jobs. AI-driven systems are being used to automate tasks that were once done manually, allowing companies to save time and money and focus on more important tasks. AI-driven systems are also being used to analyze large amounts of data, enabling organizations to make more informed decisions and improve their operations. With the rise of AI-driven systems, new high-paying jobs are emerging that require specialized skills and knowledge. AI-driven systems are powering a variety of high-paying jobs, such as data scientists, machine learning engineers, AI specialists and more [1].

Data science has been dubbed the coolest job of the 21st century -- beating out a tough field including game developer and professional sportsperson -- but is data science even a profession yet? Clearly, data scientist as a job is one of the new kids on the block, which is a cause of some of the coolness -- but may not be so great after five years or twenty years. How far has data science developed as a profession? When can we properly call ourselves a profession, and expect the kind of recognition ( way beyond coolness) that surgeons and lawyers expect? Within data science there is the beginnings of an understanding of which skills should be considered core, but not it stops a long way short of being shared to the extent found in other professions.

It's not hard to find a good story in the tech industry. The problem is that due to the industry's staggering gender gap, most of these stories center on the struggles and accomplishments of men. In this article, we aim to provide a platform for female technologists to share the stories of how they got into engineering, the biggest challenges they've faced, and their advice to the next generation of women in tech. You'll meet a former geologist turned product manager, an academic who fell in love with data science, a senior tech leader who discovered her dream job after the first two companies she worked for folded, and more. CCC's technology solutions are designed to increase connectedness among companies in the automotive industry, including insurance carriers, manufacturers, parts suppliers and collision repair shops. Ranjini Vaidyanathan was in academia and earned a PhD before realizing she had a passion for data science. While changing focuses wasn't always easy, Vaidyanathan said the transition was made easier by some simple, yet powerful, advice from her mentors. "When the going gets tough, what'll help you pull through is your passion for the technical work." How did you get into engineering? I studied applied science and mathematics before finally switching to data science after my PhD. It took me some time to decide what, exactly, I wanted to pursue. I had been doing pen-and-paper theory work as a student, but after a certain point, I realized I found applied problems more interesting. What's the biggest challenge you've faced in your career, and how have you worked to overcome it? Switching fields from academia to data science was challenging. I had to brush up industry-relevant skills like programming, and also adjust to the paradigm shift in thinking, both in terms of technical and soft skills.

In a great plot twist, instead of robots taking our jobs, they're actually helping us get hired. Artificial intelligence is becoming more prevalent in hiring and recruiting. Talent scouts who may have started using AI to test the technical ability of programmers and coders are beginning to expand their use to non-technical roles. Soft skills are more in-demand than ever, but screening a candidate for things like leadership, communication, and empathy can be time-consuming and difficult. Luckily, using psychometrics and attitude testing, AI is now equipped to assess traits like extroversion, conscientiousness, and teamwork.