How do you keep up without burnout?
Our take
The rapid evolution of technology, particularly in data science and artificial intelligence, can often feel overwhelming. As highlighted in a recent discussion, the addition of AI engineering to an already extensive list of required skills—ranging from statistics and traditional machine learning to cloud deployment—poses a significant challenge for professionals in the field. This phenomenon raises critical questions about the sustainability of learning and development in a landscape that demands constant adaptation. The expectations set forth by employers, especially when they list a laundry list of competencies for a single role, can lead to burnout and frustration. This issue resonates deeply with the insights shared in our articles like Interview Experience: Big teams look for potential, smaller teams look for how fast you can instantly come add value and Thoughts on DS I worked with inside vs outside FAANG, where the nuances of job expectations in different environments are explored.
The current trend towards expecting a single candidate to possess a vast array of skills reflects a broader cultural shift in the tech industry—one that prioritizes versatility and breadth of knowledge over specialized expertise. This is particularly true in companies outside the FAANG umbrella, where candidates are often expected to juggle multiple roles and responsibilities. This expectation can lead to a paradox where the very skills that are meant to empower workers instead contribute to their stress and fatigue. The question then arises: are we setting ourselves up for failure by demanding an unrealistic skill set from individuals, or is this a necessary evolution in response to the demands of a fast-paced technological landscape?
Moreover, this situation is exacerbated by the rapid pace of innovation in AI. The introduction of AI tools into data science workflows is intended to simplify and enhance productivity. However, the reality is that these tools often add another layer of complexity, requiring professionals to not only learn new systems but also to integrate them into their existing knowledge base. As professionals strive to keep pace with these developments, the risk of burnout increases. It's crucial for both individuals and organizations to find a balance—acknowledging the need for continuous learning while also recognizing the importance of mental health and well-being in the workplace.
As we look to the future, it becomes essential to advocate for more realistic expectations in job descriptions and to foster environments that support ongoing learning without overwhelming employees. Organizations can take proactive steps by offering targeted training programs and supportive resources that allow individuals to acquire new skills at a manageable pace. Ultimately, the goal should be to create a culture where learning is seen as a journey rather than a race.
This evolving landscape invites us to reflect on how we can better align the needs of organizations with the well-being of their employees. As the demand for multifaceted skill sets continues to grow, how can we create learning pathways that empower professionals without leading to burnout? This question is worth contemplating as we navigate an increasingly complex world driven by technological advancements.
DS sometimes feels like there's infinite amount of things to learn. Most recent trend has been AI engineering
And it's not like AI came in so you can deprioritize something else, but instead it just gets added to the heap. So you already had this massive amount of content to know from stats & product, trad. ML, deployment, ops, engineering, cloud, etc. and then you add the new thing on and the new thing. And when you read the job descriptions they literally list of all of this. I just had an interview for a random gaming company that wanted cloud, snowflake, stats, ML, ops, and AI experience in 1 person and it was for like 3-5 years of experience. And I wish that this was a one off thing but it seems to get more common.
It actually feels like FAANG is easier to interview for because they silo people and not expect you to know and do everything
What is your strategy for learning these skills without getting exhausted, or do you feel companies expectations are overflated? Is this a by product of AI where people are expected to do a lot more with less?
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- Interview Experience: Big teams look for potential, smaller teams look for how fast you can instantly come add valueMy interview experience has been a massively varied at this point, but what I've noticed is the massive difference between big companies like FAANG and smaller orgs like DS in banking or random small companies At FAANG it's kind of like an IQ + knowledge test (what google calls Role related knowledge) and smaller companies do assessments for very specific types of modeling or use cases, like build a model being evaluated on a certain metric. So at FAANG I was asked questions like "why is the formula for s.d. different for pop. vs sample', or 'what happens to the bias/variance in x,y,z situation' mean while at companies that are smaller and pay less they sent me a random 30-60 minute assessment and asked me to directly clean data and code up a model with sklearn/pandas. Is this what everyone else has experienced? It does seem like at smaller or traditional companies test if you will be a good code monkey while others look for actual understanding. submitted by /u/LeaguePrototype [link] [comments]
- Thoughts on DS I worked with inside vs outside FAANGI get ask the question online and in person: what it takes to get into a good FAANG company? I spent the last year working at a Google as DS and spent the previous 3 working at random industries (pharma, supply chain, large buy-side banks, etc.) I genuinely think that the quality of DS I worked at in FAANG were higher caliber for the following reasons: All my teammates weren't necessarily experts at a lot of things, but they had a very good grasp of the fundamentals. If you take the DS skill tree divided up into categories (ML/coding, communication, business/product sense, etc), my teammates were at least a 7-8/10 on all of these while being expert level at some things the team was responsible for. While doing mock interviews, what stood out the most is how badly some people commuinicate . I understand that a lot of people working in STEM have English as a second language, but that's not taken into considerationg when evaluating if they want to work with you. Also, I worked with a lot of DS that score very low in some aspect of what I would consider 'fundamentals'. Some knew how to code and develop, but never took a probability class. Others had heavy math background and had no idea what to do outside a notebook. Others had a good industry experience but weren't sure how to quantify their ideas and turn it into a stats problem. At Google everyone could reliably do everything to an acceptable level, and learn how to do it better if they needed to and everyone had a good 'vibe' that made them fun to talk to and work with. Honestly, the best part of the job were the coworkers while the work itself was pretty boring. I think I was picked for the role since it was a communication heavy role and I had a lot of experience coaching people and public speaking To land a job at these companies I don't think you need to be an expert specialist for the large majority of the positions. I think what you get evaluated on is if a DS problem is thrown at you, or you are in a discussion about a problem, you know what is being discussed, how the problem is solved generally, or know what to look up to solve it. If you have the extensive knowledge and experience + the things listed above you'll likely get promoted to Staff level pretty quickly or hired there. So, my final thoughts is if you are studying for these positions, don't spend your time deep diving into niche topics or doing quant style problmes. Instead, have a very good baseline understanding of the fundamentals of what DS does and be able to communicate well and demonstrate that you can contribute. For companies that can be highly picky (FAANG, MBB, etc) you also need to pass the airport test: How would I feel if I was stuck at an airport with you waiting for my next flight? submitted by /u/LeaguePrototype [link] [comments]