Health Rhythms’ Post

What a difference a year makes! A year ago today I was told those dreaded words: "you & your whole team are being made redundant..." I must admit it really shook my world. After 14 years at the same company it was hard to imagine working somewhere else. And yet - and I know it's a cliché - there is life beyond redundancy. For me, the redundancy was the nudge (more like "shove" actually) that I probably needed to set up my own Behavioural Science consultancy, Unconventional Wisdom - first as a one-man-band, then with Patrick Sharpe as our client base grew. It's not all been easy (understatement!) but it's incredibly rewarding. We're now on 11 clients, who are mainly using our Behavioural Science Skillset "on tap" - so they can ask for our input, review & ideas whenever they want (e.g. when building a new online journey or customer comms) - for a few days or even a few hours at a time here or there. As the CTO at one of our clients said recently: Will, you guys really "augment" our team. So if you'd like to become our 12th client - do reach out and let me know. There may even be a free bar of chocolate in it for you. And for anyone out there who is facing redundancy or uncertainty in their career: take courage! And feel free to reach out to me if you'd like to talk things through - I'm happy to share ideas & career tips - e.g. if you're early on in your career in behavioural science. Finally, I'm extremely grateful for key people in my network who enabled me to see - and grasp - new opportunities: Nicholas Howard, Thomas Weilbacher, Liam McDermott, Marco Kamerling, Elizabeth Wesson, Vanesa Cividini, Andy Slade, Felix Haas, Weiting Bollu, Mark Rebuck, Alison Mclean, JP Hughes, Aideen McGrath, Donna Cowell, Gwilym Pugh, Bronwyn Kirwan, Rebecca Clapham, Joshua Sargent, Darren Coomer, Julien Descombes, Alex Holdsworth, Jez Groom, Silvan Stuessi, Donna Cowell, Anuraag Verma, https://lnkd.in/eDNrTv5F

Company reputation absolutely matters when trying to attract top Data, Machine Learning, & AI professionals. Quick story time. A position opened up with one of my clients and I launched a search. I came across an accomplished Staff Data Engineer from big tech and reached out immediately. He responded and told me he wasn’t interested. It turns out before this company was my client, he interviewed with them for a different opportunity and made it to the final round. But after the final interview, he never heard back from them. I let my client know the situation and they addressed it. Didn't happened again. The fact they were willing to eat their mistake and make necessary changes was great to witness. But the point is, candidates remember how they’re treated. Sometimes, occurrences like this lead to poor Glassdoor reviews or being called out on Reddit, Inc. threads for everyone else to see. Ghosting is one thing among many that can negatively impact company reputation. Poor company reputation can be especially detrimental to startups who are trying to build and scale. How's your company reputation and what areas could you use work in?

As a former #enrollmentmanager, I never considered improving time as a way to impact #yield and morale. I should have. By reallocating 1 hour/day away from data wrangling (query running, report building, etc.), our enroll ml partners are increasing team capacity by 30%! How does this impact morale? Helping #admissionscounselors move closer to the reason why they love their job: connecting with students and families. #highered #enrollmentmanagement

I never got Master’s degree in Data Science. I never worked at Big Tech. I never got a 200k salary. When all of my friends and ex partners were building their tech careers, I was ridiculed for having too many passions and not mastering any. I was told I will never succeed if I won’t follow the same path as they did. I believed them. So I stayed in toxic relationships. I allowed disrespect and abuse. I undervalued my own effort in my unique pursuits. I endured the suffering thinking I deserved this. Until I reached an emotional burnout. When my mental health hit rock bottom, I decided to look inward. And finally take courage to choose myself. To write my own story. To follow my own curiosities. To chase my own dreams. Despite the loud judgmental voices around. And here is where I am now: - I built an audience of 20k engineers and scientists across all platforms in 3 months. - I lead a community of 1000 active learners who are helping each other grow. - I deploy and build multiple AI-driven applications, projects daily. Don’t let opinions of others get in your head. Don’t make yourself small to please the fragile egos around you. Don’t underestimate your own achievements, just because you don’t have a random paper that dictates your worth. Remember if you don’t choose yourself, no one else will. Now, go and make yourself happy and proud. P.S. I cried writing this. Hope it will remind you of what is really important. ❤️ You 🫵🏼! #mentalhealth

🚨3 Things I wish I knew before starting my Career in Data Science 🚨 1. Delayed Gratification 🙏🏻 We tend to look for immediate reward for our effort and in hindsight let go of a more-valued reward in the future. We need to understand instant gratification leads to temporary Dopamine, not a fulfilling career. Invest in understanding delayed gratification for your efforts you put into your work. You do not need the “Employee of the Month” award, rather you should deliver a distinguished and high quality solution. 2. Hitting the Career Plateau 🚫 I have spent months working on projects and learning very hard but seemingly had any significant progress. I felt stuck because I had hit the plateau. We need to take control of our career, move out of the comfort zone and challenge ourselves to break through that plateau. 3. Quarter-Life Crisis 😥 I was 25 when I transitioned into Data. It was a happy start which did not last very long. I used to struggle with anxiety, seeing people, who started with me, do better than me. I have questioned myself several times whether this line of tech is for me. We need to find the right tools to deal with anxiety and understand what could give us a boost in our career, instead of just waiting for time to do its thing. It is a process to navigate the mental health side of our life and there is no “one-size fits all” solution. We need to indentify our problems and stay accountable of the process. ➿ #datascience #dataanalytics #mentalhealth #job #career

What does your Talent Stack look like? I’ve spent a lot of time trying to job hunt, network, and showcase my skills in this nonsense job market, so I recently took a break. Instead, I’ve decided to take an inventory of “what is” so that I can uncover “what will be”. A talent stack represents the combination of skills we each have that make us unique. Coined by Scott Adams’, it can be used to define gaps or areas of untapped potential. Each of our talent stacks gives us the potential to be extraordinary if we add skills in the right way (toward talent, interest, and uniqueness). When the skills combine – POOF! We are extraordinary! I've added my talent stack below. It's great that Scott acknowledges that he's moderately good at a few things but when they are combined he is extraordinary! ME TOO! OMG he and I have SO much in common!! I'm - maybe - 7/10 good at program management but look at the stack! In a one-sided role like that (ie not also focused on my other talents like data, ML and juicy problems), I will not excel. I will also not excel with a one-sided manager, in a one-sided company, or in a one-sided job market. 🤷 As for what’s next, I’m checking out my untapped potential in areas like:   🎯 AI/ML: the inner workings, and how/when to use it to transform the business (more coming on this!) 🎯 Data consumption: interpretation, visualization and actionable insights  🎯 Project and program "therapy": Evaluation and course correction of under-functioning programs. The Doctor is In! 💉 Credit where credit is due: https://lnkd.in/eKg7EaJG and special thanks to Coach Diana Strong (Colley) who has been instrumental in my journey. #talent #aiandml #dataanalytics #storytelling

I've been pretty adamant throughout my professional life up to now that I would resist going to graduate school. I have developed a tremendous portfolio of technical, organizational leadership, and delivery execution skills through my job experiences and didn't think it would contribute meaningfully to what I could bring to the table for prospective bosses or new employers. As I've become more hooked on AI/ML and hoping to deliver leadership in that space as I move forward, my confidence in that stance is breaking down. I'm wondering if getting a Masters of Engineering in Data Science or AI/ML would be a wise investment both to solidify my domain mastery, but also to signal that mastery and the hunger for the space to potential employers. It would be a big time and effort investment over probably 24-30 months interleaved with gigs, so I'm wrestling with whether it's a productive move. I'm only 10 blocks from the University of Pennsylvania in West Philly, so if I can swing it, I could go to school in the neighborhood. AI/ML is new enough that I suspect lots of enterprises are using advanced degrees as a checkbox for the roles I'd be most interested in because there's a relatively small cohort of folks who can show extensive experience on the job. Of course, that's a highly speculative assertion on my part. Many folks in the technical executive space that I operate in have gone for MBAs. While that would undoubtedly open some doors it would likely be the kind of doors I'm somewhat less interested in walking through. I'm sure that I could deepen my understanding of gross margins, ROI, LTV, CAC, ARR, risk assessment, etc., but I think I can deliver far more value to my employer by bridging that world into the complex landscape of modern architecture, development, delivery, and operations. I know that plenty of folks amongst my connections have advanced degrees. I suspect many others have considered at various points whether to take the plunge. I'd love to solicit y'all as to your thoughts on the wisdom of making such an investment in time, effort, and $. What say you?

If you’re graduating and looking for a job in data, conventional wisdom says to join a big company and work your way up. I think that’s extremely inefficient. While there’s lots to learn from big companies, their sheer size can often confine you to only learning your specific role, and not how it relates to the entire system. That’s why if it were up to me, everyone would join an earlier stage company—doesn’t have to be Pre-seed or Seed, but somewhere where you sort of have to do everything. This will help you learn "the system" end-to-end, instead of being stuck as a cog in the machine and not seeing the other parts of it. "The System" here is how to deliver the value of your actual job to the business end-to-end. Once you do this for a bit, you’ll realize whether you’re a startup person, who likes smaller and more dynamic teams—or someone who’d rather work on a bigger team at a FAANG. And if you would rather work at a bigger company, you can come in with a higher level of understanding around how the whole system works compared to folks who’ve only ever worked as part of a big team. Working at a startup earlier in your career gives you the experience to make a better-informed choice later on, and a better understanding of what really drives the value your managers will care about regardless of company size. What do you think? Agree or disagree?

The OpenAI situation is a helpful prompt to reflect on how you feel about your CEO, as well as your direct manager: 1) How would you feel about your CEO being suddenly fired out of nowhere? 2) What about your manager? If your reaction is sufficient discontent and regret that you'd sign a public petition and commit to joining a new company to follow them, that's a great sign you're in the right place. If your reaction is relief and "Yes!" that's a great sign that it's time for some soul-searching soon. I've written before about Managers imagining how they'd react to each team member sharing that they're quitting: deep regret is great; deep relief is quite the opposite. The whole exercise takes 10 seconds max per person. This is the same concept; it's just going up the reporting chain vs. down. Ideally, most of your reactions would be akin to the 700+ OpenAI employees who so publicly supported Sam Altman. If so, a) be grateful, and b) consider sharing that gratitude with your boss and/or CEO, especially given it's Thanksgiving in the US.

Don’t know what role to pick in tech? Here is a guide for you with a touch of humor… 👇 Before starting a job search you need to understand your options thoroughly. The following will illustrate the functions and frustrations of each role with a touch of satire. ➡️ If your idea of a fun weekend is arguing with a machine in a language only 2 % of the population understands, congratulations, you're ✨ 𝐚 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫✨. You'll spend more time debugging code than sleeping, but hey, that's just more time to admire your beautiful error messages. ➡️ Love telling engineers what to do without doing the coding yourself? Dream of making grand plans that change with every client meeting? You might like being ✨𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐫.✨ ➡️If you find joy in turning a mountain of data into an insightful line graph that upper management will ignore, then ✨𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 ✨might be your calling. You're the corporate equivalent of a fortune teller, but with spreadsheets instead of a crystal ball. ➡️ Want to build the next algorithm that decides which ads haunt people on the internet? As ✨𝐚 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫✨ , you’ll play with more data than you can imagine and occasionally worry about the ethical implications at 3 AM. ➡️ If you love the idea of spending hours refining models that might just end up predicting that people buy more umbrellas when it rains, ✨𝐚 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭✨ role is for you. It's like being a detective, but instead of solving crimes, you’re figuring out why sales drop every Tuesday. ➡️If you like doing all of the above and more. Then this special position might be for you. A modern-day superhero, juggling finance, marketing, product development, and your sanity all at once? ✨𝐀𝐬 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫✨, you’ll have the unique opportunity to work 100-hour weeks for yourself instead of 40 hours for someone else, all while reading more self-help books than you ever thought possible. Pick your role wisely. Happy hunting! Follow Ashish Singh 🇮🇳 for more insightful content! Note: The views and opinions expressed in this posts are those of the author and do not necessarily reflect the official policy or position of Novartis or any of its officers. #data #dataanalytics #dataengineering #datascience #datacareer #careeradvice