Digitally native companies like Google, Uber, Amazon, and Netflix have revolutionized the IT industry with cutting-edge machine learning capabilities and are constantly seeking highly skilled talent in the field of Artificial Intelligence and ML. Now, the next generation of startups are bringing products and services to market to address industry-specific challenges.
However, as the ML ecosystem has grown, the need for experienced professionals has multiplied. The buzz around ML has spawned a plethora of online courses that provide a good stepping stone for beginners, but the learning curve is often steep. While online courses on popular MOOC platforms are certainly useful, companies and startups are looking for professionals who have experience with ML technologies and have translated their work into products and features. But as the community around ML has grown, so has the competition. So, one of the key questions of our time is: “How do I differentiate myself from the crowd?” There is currently an oversupply of self-taught STEM learners, so to differentiate, you need to leverage Kaggle or Github platforms.
To compete with your peers for employment, it's not enough to have theoretical knowledge – most organizations are looking for top-notch, business-minded ML experts who can apply the technology to business problems.
according to Matthew J. Schwartz According to New York-based recruiting firm MJS Executive Search, top companies prefer ML candidates with the following skills:
- Business experience plus PhD in Machine Learning, Mathematics and Computer Science
- Knowledge and experience of cutting-edge open source platforms
- ML professionals also need to have the ability to engage and consult with both business leaders and data scientists.
- Preference will be given to researchers with the most cited papers, published journals, patent applications, and speaking engagements.
- Experience with cutting edge ML technologies
- Deep learning, AI-optimized hardware, decision management, biometric authentication
Those working at the intersection of Engineering and Big Data ecosystems are exposed to data modeling and building end-to-end production systems. Software engineers usually follow the route Software Engineer < Data Engineer and usually work with descriptive statistics. So, if you are a software engineer and are trying to understand the ML ecosystem and planning to make the switch, here are some tips taken from the community forums:
- ML candidates should boast a strong coding background (especially Python) and also have a solid foundation in statistics, linear algebra, and calculus.
- Unlike large US-based technology companies, most Indian companies do not expect candidates to have published research papers.
- What is more important are their Github profiles and successful ML projects run as side hustles in startups and small businesses.
- Another effective way to get to know yourself is to improve your research on real-world applications.
- Also, domain knowledge is preferred for certain jobs: someone with an NLP background is expected to know how to convert graphemes to phonemes, for example.
- On the other hand, if you're a complete beginner, look for mentorship, projects, and repositories and start working in your spare time.
The last word
Data engineering and ML experts are in high demand, and companies struggle to fill these positions. Finding and attracting ML and data science talent has become a strategic imperative for all companies. Also, hiring cycles are usually long, and one of the reasons for this is a lack of clarity in the business problem, points out Yann LeCun, Director of AI Research at Facebook. MJS ResearchMost technology companies can’t figure out the business problem and how to solve it.
Speaking to Shridhar Marri, CEO and Co-founder, Senseforth, he said that ML beginners should go beyond online learning and demonstrate their enthusiasm with projects. “Online courses, especially from universities abroad, are a good starting point, but beyond training, it's also important for ML enthusiasts to demonstrate their work with solutions. There are a lot of open source and ML platforms available for beginners to explore,” he said.
