I receive several messages about the benefits of joining FAANG and similar companies and startups in the context of Data Science, Machine Learning & AI roles.
Here’s my take, in no particular order:
1. 𝐁𝐫𝐚𝐧𝐝. FAANG+ are not only the top technology companies but also the biggest companies by market cap -> great brand to add to your profile, top compensation and benefits.
2. 𝐒𝐜𝐨𝐩𝐞. The scope of AI/ML applications in these companies is tremendous as they have tons of data. You can get to work on multiple use cases, driven by statistics, machine learning, deep learning, unsupervised / semi-supervised / self-supervised, reinforcement learning etc. Internal team transfers facilitate expanding your breadth of ML experience.
3. 𝐁𝐚𝐫. The AI/ML work is cutting edge, as most of these companies invest heavily in R&D and create game-changing techniques and models. They also invest heavily in platform, cloud, services etc. that make it easier to build and deploy ML products.
4. 𝐑&𝐃. You can do both research on moon-shot projects if that’s your cup of tea, as well as more immediate business-driven data science projects with monthly or quarterly deliverables.
5. 𝐏𝐞𝐨𝐩𝐥𝐞. You get to work with the creme-de-al-creme in terms of talent, ideas, vision, and execution. Your own level will rise if you are surrounded by some of the brightest folks, and also get to collaborate with their clients and collaborators from academia, startups as well.
6. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤. After FAANG, people go on to do many diverse things — from building a startup to doing cutting-edge research to non-profits to venture capital amongst others. You can find quality partners for the next steps of your career journey.
7. 𝐒𝐲𝐬𝐭𝐞𝐦𝐬. Processes and systems for AI/ML/Data are more mature and streamlined than smaller/newer companies which can facilitate your speed and execution of your projects.
8. 𝐂𝐮𝐥𝐭𝐮𝐫𝐞. The culture, on average, is more professional as these companies invest heavily in their employees and regularly come up with new employee-friendly policies to make it a great place to work.
9. 𝐅𝐫𝐞𝐞𝐝𝐨𝐦. After FAANG, you will be in demand and recruiters and hiring managers will seek you out if you’ve proved your chops whilst at the company. You will have more opportunities to sample from and greater freedom in terms of deciding your career and life trajectory, as you can also move internally to different countries.
10. 𝐈𝐦𝐩𝐚𝐜𝐭. Given the scale at which these companies operate, the scope for real-world measurable impact is enormous.
There are some downsides, caveats and exceptions as well, but on average these factors make FAANG and similar tech companies a very attractive proposition to launch, build and grow your career in data science and machine learning.
Published in BusinessWorld
The promise of AI is real. Research from Accenture posits that AI could add $ 957 billion to the Indian economy and raise India’s income by 15 percent in 2035. Globally, the economic value that AI is expected to create close to $ 13 trillion by 2030. However, the stark reality is that India has close to 100,000 vacant data scientist jobs as of today, with the demand for AI-centric roles set to increase exponentially. How can India possibly unlock this massive economic potential of AI, without an established talent pipeline?
The lack of an established AI talent pipeline for a rapidly modernizing economy like India is alarming. While India has a working age population of close to 589 million, only 49 percent are said to possess digital skills, with the proportion of those able to understand and build AI products is far lower (World Economic Forum). Although the supply of engineering talent is steady, the nature of the rapidly changing jobs landscape means that core engineering jobs are transforming into digital roles that require strong software engineering and programming skills.
Not only Indian universities have failed to keep pace with adapting the course curricula to the skills requirements of the modern data-driven industries but the consequences of not training candidates in fundamental data skills and leadership skills to build collaborative AI projects can be even more damaging to the economy in the long run. Academia suffers from an acute shortage of expert faculty to train students in state-of-the-art AI theory and practical knowledge at scale. This burden of nurturing and creating AI talent does not rest solely with educational institutions. Industry needs to step up and actively contribute by sharing business data, a critical ingredient for building data-hungry supervised AI systems, and foster a vibrant and collaborative ecosystem by partnering with both academia and startups to raise awareness of the kind of challenging business problems that only AI can solve effectively.
To bridge the gap between industry requirements of AI talent and lack of industry- oriented AI education at universities, a number of edtech startups have stepped up. The majority of online edtech platforms focus on programming and coding skills, a key foundational skill to building AI systems. However, the pedagogical methods practised by most suffer from lack of imagination and creativity and do not innovate beyond offering the age-old offline classroom content via online platforms - the adage ‘old wine in a new bottle’ comes to mind.
AI is a multidisciplinary field that requires strong creative, scientific and problem solving abilities to come up with novel solutions to pressing business problems. The ability to innovate beyond open-source models and solutions is fundamental to building tailored customer-centric AI solutions that incorporate the unique business and cultural context of India.
If India is not able to keep pace with AI global superpowers like the USA and China, then not only is she at risk of lagging behind in the battle for tech supremacy but also faces the dire prospect of losing its emerging tech talent to countries that offer better opportunities to work at the cutting edge of AI. India is set to become the world’s youngest country with 64 percent of its population in the working age group, while western countries, China and Japan have an aging demographic. India must therefore implement policy changes, state-wide reskilling initiatives in cooperation with industry, academia and startups to reskill the nation’s youth in the latest digital and AI-first skills to steer India into the next decade as a leading digital economy.
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