Resha Kolvalker’s Post

Preparing for data engineering interviews is stressful! You need to be thinking about data structures and algorithms, data modeling, data architecture, behavioral, and past projects to truly land the role of your dreams! In this article, I go over: - How I prepared for the staff data engineer interview I took at Airbnb - How I got multiple offers between Meta, Google and Airbnb - How I negotiated for more money We're currently doing Black Friday sales for DataExpert.io! You can get 30% off right now! #dataengineering

Being a Generalist Data Scientist won't get you hired. Here is how you can specialize 👇 Companies have specific problems that require certain skills to solve. If you do not know which path you want to follow. Start broad first, explore your options, then specialize. To discover what you enjoy the most, try answering different questions for each DS role: - 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 Qs: “How should we monitor model performance in production?” - 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 / 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 Qs: “How can we visualize customer segmentation to highlight key demographics?” - 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 Qs: “How can we use clustering to identify new customer segments for targeted marketing?” - 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫 Qs: “What novel architectures can we explore to improve model robustness?” - 𝐌𝐋𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 Qs: “How can we automate the deployment of machine learning models to ensure continuous integration and delivery?” --- As a data scientist, what questions do you enjoy answering?

That long minute of dead silence that fell across Chicagoland last night was me watching 20 years of know-how flash across the screen while our new “AI” inspected our data model and spit out perfectly executable SQL in response to every one of my questions As my mind went from “WTF” to “Now what?” to “This thing is freaking beautiful” (my actual internal voice is NSFW), I had to ponder if my work here is about done. In the realm of targeting jobs for automation, this was the first true glimpse of what’s about to come. It’s easy to argue that a CEO has other things to do than write SQL. This is true, and while its a guilty pleasure of mine, I’ve lost track of how many young data scientists that have been imparted with the secrets of industry context and the alchemy of creating something beautiful and actionable out of otherwise data chaos. Now, instead of hiring and training people, we’ve trained a model. Forget my own hobbies for a minute, and multiply this impact across every data or business analyst role out there. Your job is now redefined. Into what? Hell if I know yet, but it’s time to learn how to make these tools work for you before you work for them. My job now becomes codifying outcomes into a platform and scaling its reach while helping people navigate these transitions. That work will likely never be done. The other take away is that we are 100% going to reinvent BI in the Age of AI. The UX and frontend might not all be fully in place for the launch on 4/24, but the APIs and infrastructure are well on track. #AI #contactcenter #bi

It's time to get creative with my job search, which means for now, doing a retrospective on some of my favorite video projects over the years... For anyone who finds this in their feed and doesn't know me - hi! 👋 🙂 I'd label myself as a grade-A strategist and problem-solver who loves working at the intersection of community and communications. In 8 years at an advertising technology startup called AppNexus I helped build 3 teams (Audit Services, Recruiting, & Marketing) as well as every role that I had at the company. I worked cross-functionally with literally every team in every timezone, and ended my career there as Head Storyteller. Prior to AppNexus I worked in the theater, post AppNexus - I did naming/branding/design/video production out of my home in Brooklyn till the pandemic mused me to find work in the natural world 🌲 ...first working with animals and now currently as a property manager and (literal) trailblazer. I'm seeking an internal communications/community marketing role - but open to discussing other options. Ideally I'll find work with a company trying to solve some part of the climate crisis, that's where my heart is. Hybrid/remote/relocation - everything is on the table. (Not particularly looking for video production work, just thought it would be a fun way to stroll down memory lane and get this search going.) 🌳 🍄 🐢 🌊 🌤 OK first video: Intro to Data Science TL;DR - this was a video to help colleagues at AppNexus understand how our Data Science team works and advertise for the Razzle Dazzle (the most fun company all-hands in the world). STORY TIME - Even before I was Head Storyteller at AppNexus I used to host a weekly consultation hour in our NYC lunchroom called Super Serious Razzle Dazzle Consultation Hour. Colleagues from every level and across every org would swing by for marketing and/or community advice. ...and one time our Chief Technology Officer Catherine Williams wanted a video made to help our global AppNexus community understand what her Data Science team was responsible for... Which led to Stephanie Tzeng visiting my SSRDCH to make the request... Which led to me saying "Yes AND..." resulting in an internal marketing campaign, a week of Data Science elective classes produced by the team, and led up to a Data Science Razzle Dazzle for the whole company on Pi Day. The promo video was also used for recruiting purposes. The Data Science team mused me to draw inspiration from Wes Anderson movies. Thoughtful, witty, specific, playful... It was such a fun collaboration. Special shoutout to the DS team & Steph (my co-writer and the star of the video!) as well as Brian O'Kelley for the cameo, and my old boss Euan Lampitt 🧡 🖤 If you've read this far - I'm so impressed. This is a very long post 😂 Let me know your favorite climate-solution-focused company to keep an eye on! https://lnkd.in/e-C8W5cD

🚨 Super exciting role alert: GTM Data Scientist!! 🚨 If you've followed me for any time then you know one of Owner's super powers is our use of data and systems. I did an entire SaaStr Workshop on it that I'll link below. Jonathan Shenkman is the mad scientist behind a lot of it and he's hiring right now. I think this role is so exciting because you get a chance to work hip to hip with executive leadership, impact the most important strategic decisions at the company and be a true partner to the business. A bit more about the role: We're on a mission to build an exceptional data-first operations and GTM function at Owner, and we're searching for a Data Scientist to help us achieve this vision. If you're passionate about data-driven decision-making and thrive in a high-volume B2B environment, this could be your next big opportunity! As our Data Scientist, you'll: 🔍 Develop and refine data-driven systems that power our GTM team and enhance our business operations. 📊 Contribute to a culture of data discipline and efficiency, working closely with our Director of Business Operations. 💡 Build solutions for lasting processes and systems, leveraging AI, automation, and deep data enrichment to optimize our GTM pipeline. p.s. My LI inbox is a mess so please email if you're interested (kyle @ owner) p.p.s. Don't ask me why I thought this rocket ship chef made sense for this post 🤷♂️

I found this interesting!😶🌫️🕊️ Skill Up and Get Paid What You're Worth💸 visit: https://datanerd.tech/ 💫 Goal Open-sourcing skills and pay requirements are necessary to help data nerds avoid wasting time pursuing irrelevant skills and underpaid jobs. This app is only the beginning of that journey. #data #datascience #datascientist

𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁. Three titles you hear everywhere. But what do they really do? 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 handle the backend. They design pipelines to move and store data. They make sure it’s clean, organized, and ready. Think of them as builders laying a strong foundation. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 focus on the present. They analyze data to answer specific questions. Spot trends. Create reports. Build dashboards. They translate raw data into actionable insights. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 explore the future. They use algorithms and models to predict outcomes. Their work is experimental and forward-thinking. They deal with uncertainty to uncover possibilities. Without engineers, there’s no data to analyze. Without analysts, insights stay buried in numbers. Without scientists, there’s no vision for what’s ahead. Each role depends on the other. That’s how great data teams work. Simple, right?

Hey Friends 👋 I need help. In the last two years since I joined Stan, we've scaled from $1M to $30M ARR. It’s been an awesome journey with a phenomenal team! But I'm still the only DS and things are starting to break :D ✅ How you can help: if you know any exceptional Full Stack Product Data Scientists please tag them below so I can reach out and give them an amazing offer. 🛸 How to know if they’re the right fit: if your life depended on their DS abilities to turn a product around and make it successful would you be happy to pick them to be on your team? Here's the JD: https://lnkd.in/gAwTGKTv #DataScience #Hiring #ML #GenerativeAI #Growth #Stan

𝐖𝐡𝐲 𝐫𝐞𝐚𝐜𝐭 𝐭𝐨 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐰𝐡𝐞𝐧 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐩𝐫𝐞𝐝𝐢𝐜𝐭 𝐚𝐧𝐝 𝐩𝐫𝐞𝐯𝐞𝐧𝐭 𝐭𝐡𝐞𝐦? 𝐋𝐞𝐭’𝐬 𝐫𝐞𝐢𝐦𝐚𝐠𝐢𝐧𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫. From reactive to proactive: Data analytics reimagined. Old analytics worked like a rear-view mirror. It only told you what happened. Now, real-time analytics is the game changer. It’s powered by edge computing and advanced tools. You can now act as soon as the data speaks. Sectors like finance, healthcare, and e-commerce thrive on this. Imagine reducing fraud in seconds, saving lives, or boosting sales instantly. In my GitHub projects, I’ve built systems focused on data speed and clarity. One project transformed static dashboards into real-time decision hubs. Another automated alerts for critical insights, reducing delays drastically. I’ve lived this shift in analytics for over 3 years. And I’m ready to bring this impact to your team. 📌 Let’s connect if you’re hiring! #dataanalytics #realtimedata #innovation Balemarthy Vamsi Krishna Anushika B.

The question isn't IF you should hire generalists or specialists. The question is WHEN you should hire generalists or specialists. Let me explain. It all depends on what your business needs in that moment. Ex 1: You can be a mature business with no clear GTM strategy where you’d need a generalist. Ex 2: You’re an early-stage startup and need an experienced data engineer, so you’d need a specialist. 3 things you need to look at: ➝ Where you are in the business ➝ Where you are in the roadmap ➝ How mature your roadmap is But here's a general rule of thumb: The more ambiguity there is, the more you need a generalist. Because the last thing you want is to over-specify on skills or traits and realize you’ve got the wrong person.