Career Pathways in Technology

If you’re AI-curious but can’t decide where to start, this one’s for you 👇 The AI space is vast. Buzzwords fly. Roles overlap. And it’s easy to get stuck wondering: 👉 Should I become a Data Scientist, ML Engineer, or Product Manager? Instead of chasing titles, map your strengths and figure out where you fit best in the AI lifecycle. 📌 I put together this infographic + a blog post to help you find your lane, with 10 clear roles you can actually train for (even without a PhD or a Stanford badge). 🚀 The 10 Career Paths in AI, Simplified: ➡️ AI/ML Researcher or Scientist – creating new algorithms, publishing papers, pushing the frontier ➡️ Applied ML Scientist / Data Scientist – solving real-world problems with models and experimentation ➡️ ML Engineer / MLOps / Software Engineer (ML) – taking models to production and scaling them ➡️ Data Engineer – building the infrastructure to move and manage data ➡️ Software Engineer – writing core product code with ML components ➡️ Data Analyst – analyzing data to drive insights and business impact ➡️ BI Analyst – working with KPIs, reporting, and decision frameworks ➡️ AI Consultant – advising teams and clients on adopting AI responsibly ➡️ AI Product or Program Manager – aligning AI capabilities with user needs and business goals ➡️ Hybrid Roles – wearing multiple hats across technical and strategic functions 🧭 How to choose the right one for you: → Start with your natural strengths: coding, communication, business thinking, or data sense → Identify the part of the AI lifecycle you enjoy most: research - build - deploy - iterate → Stack the right skills intentionally: • Coders: Python, PyTorch, prompt design, eval frameworks • Data Infra: SQL, Spark, Airflow, Lakehouse, vector DBs • Insights: Analytics, causal reasoning, dashboard tools • Translators: AI roadmap building, governance, storytelling → Focus on shipping evidence of work: demo apps, notebooks, open-source PRs, or experiments → Develop a T-shaped skill profile – go deep in one role, but stay conversational across others 💡 A few truths to keep in mind: → You don’t need to be a “10x coder” to work in AI → Problem-solving > job titles → Projects > perfect resumes → Cross-functional skills are a force multiplier – clear writing, ethical reasoning, and stakeholder empathy go a long way → There’s no “entry-level” in AI – just entry-level impact 📖 Curious to explore deeper? Check out the full blog, and save the infographic to use as a compass for your AI journey: https://lnkd.in/daQNHPyg

7 Steps I Used To Change Careers (From Healthcare To Microsoft) With No Experience: 1. My Pivot Story I started my career in healthcare, working as a medical device sales rep in North Carolina. Two years later, I landed a role in tech sales at Microsoft in New York City. Here are the steps I used to make that career change without any formal tech experience: 2. I Started With Clarity Before I sent out resumes, applied, and networked? I focused on getting crystal clear about the specific types of roles and companies I wanted to work for. Juggling the possibility of multiple job titles and industries is overwhelming and stressful. When you have a single north star, you can invest 100% of your time and energy into it. 3. Then I’d Found People Who’d Done It Before I crafted a list of criteria I wanted for myself: Specific Job Titles Specific Companies Specific Locations Specific Salaries Then I used LinkedIn to find people who met those criteria AND came from a non-traditional background like mine. 4. I Used Those Connections To Craft A Blueprint How do I build the right experience? How do I position my non-traditional background? What mistakes should I avoid? I used the connections I just mentioned to gain clarity on all of those things so I could craft a plan for building the experience I needed to fit the skills and narratives companies would buy into. 5. I Created My Own Experience I wanted to work in advertising technology (think Google Ads, Facebook Ads, etc). Rather than hoping someone would give me a chance to get experience? I went and created my own. I took courses, volunteered my skills for local orgs, then used those success stories to freelance. Employers want results, not education from career changers. 6. I Focused All My Energy On Networking Online apps didn’t work. People would take one look at my resume and not see any traditional experience. When I networked with people? I could have a conversation with them. I could tell my story in my own words. And I could prove my value as the relationship built. That generated referrals and advocates. 7. I Used My Background As An Advantage Sounds crazy, right? Most applicants had cookie cutter backgrounds. I told a story of how I had to learn this all myself, from scratch. I’d bring a new perspective, new takes, and new ideas to a take vs. simply adding someone with the same thought process as everyone else. That’s got buy in from a lot of stakeholders. 8. I Created Projects To Prove My Value When I landed interviews? I’d brainstorm ideas for how I could impact those and package them in a 5–7 slide deck that matched the company’s branding. I’d send it to my interviewers as proof of the ideas I could bring. Showing them > telling them. —— ➕ Follow Austin Belcak for more 🔵 Ready to land your dream job? Click here to learn more about how we help people land amazing jobs in ~3.5 months with a $44k raise: https://lnkd.in/gdysHr-r

AI Product Management vs AI for Product Management: Hacks and resources for you. Regardless the path you're on, you need to evolve your PM Craft. 'Evolve' being the keyword here. 𝗙𝗼𝗿 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (This is for the PMs working directly with AI products) – think Research PMs, Recommendations PMs, Platform PMs, and so on. You really need to get good at handling AI's unique quirks: ✨ The Probabilistic nature of AI: It's not always 0 or 1, and you've got to navigate that uncertainty. ✨ The Deep dependency on good quality data: Garbage in, garbage out. You're constantly thinking about data quality. ✨ Developing deep AI awareness: This is key but it's not about you getting too deep into technical concepts you won't need. My secret hack is to make it a habit to read research blogs from big tech companies. Google AI, Meta AI, OpenAI and attending technical conferences. Here are some: -Google AI Blog: https://ai.google/ -DeepMind's blog https://lnkd.in/g3mi8Xxy -Meta AI Blog: https://ai.meta.com/blog/ -OpenAI Research Blog: https://lnkd.in/gR_kPSkt -Microsoft AI Blog: https://lnkd.in/gYkW63yz -Amazon Science Blog: https://lnkd.in/gMJzQrGG You'll literally see what's going to be the next big product in the next two years. The original Transformers paper came out in 2017 – a PM on top of their craft could have foreseen Generative AI tools coming years ago. 𝗙𝗼𝗿 𝗔𝗜 𝗳𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 ✨ This is about leveraging AI tools to have more impact as a PM, no matter what sector you're in. It's all about adjusting your work style and experimenting to see what actually works for you. My hack here is simple but effective: train your brain to try new things. I block my calendar for 2-hour "experimentation slots." During that time, I'm creating my own tutorials, trying out new AI tools on my actual work, and following the right people. You know most of the tools by now, here are some that you might want to check out: -NotebookLM: new features getting added very often -ChatPRD: https://www.chatprd.ai/ -Productboard AI: https://lnkd.in/gm2mfeDY -ProdPad CoPilot: https://lnkd.in/gWrZZd7W -Quantilope: https://lnkd.in/g3TUJ_-9 -Dovetail: https://dovetail.com/ -Notion AI: https://lnkd.in/gfUb8yKg -Mixpanel: https://mixpanel.com/ Regardless of your seniority, being hands-on and experimenting with these tools goes a long way.

The best promotions don't go to the most talented people. I’ve worked with thousands of professionals in my career. If there’s anything I’ve noticed after a decade in tech, it’s the fact that the promotions and high-visibility projects go to those with advocates. Here are the key reasons why advocates are essential for career growth (and some practical tips to gain and nurture them) 🔵 Advocates amplify your visibility. ↳ They talk about your achievements to others. This spreads your name and work beyond your immediate circle. 𝗧𝗶𝗽: If you’re a heads down person like me, it’s time to lift your head up to build relationships with colleagues and mentors who can vouch for your skills and contributions. 🔵 Advocates provide opportunities. ↳ They recommend you for projects and roles. This opens doors that you might not even know exist. 𝗧𝗶𝗽: Show your value consistently so that advocates feel confident in recommending you. 🔵 Advocates build your credibility. ↳ They lend their reputation to yours. This enhances your professional standing and trustworthiness. 𝗧𝗶𝗽: Maintain integrity and professionalism to ensure advocates are proud to support you. 🔵 Advocates ensure your efforts are recognized. ↳ They make sure your hard work is seen by decision-makers.This leads to promotions and career advancement. 𝗧𝗶𝗽: Document your achievements and share them with your advocates regularly. Bonus: 🔵 Advocates help you build a network. ↳ They introduce you to influential people. This expands your professional connections and opportunities. 𝗧𝗶𝗽: Your network is one of the most valuable things you’ll take with you in your career. Don’t let these opportunities go to waste! Seize the opportunity to build strong advocates and supercharge your career in the final months of 2024. If you’re not sure where to find your first advocate, try building a good relationship with your manager. Here’s my FREE LinkedIn Learning Course that can help you turn your managers into your #1 advocates: https://lnkd.in/gPXXNckd 💬 What are your obstacles when it comes to finding advocates at work?

My secret weapon when I pivoted from to tech from a non-tech background: Emphasizing my transferable skills. Here's how to leverage them the right way to land the job of your dreams: 1/ 𝗟𝗼𝗼𝗸 𝗮𝘁 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 ↳ Review all your work, volunteer activities, education, and personal projects. Don't just focus on job titles - think about what you actually did day-to-day. 2/ 𝗕𝗿𝗲𝗮𝗸 𝗱𝗼𝘄𝗻 𝘆𝗼𝘂𝗿 𝗮𝗰𝗰𝗼𝗺𝗽𝗹𝗶𝘀𝗵𝗺𝗲𝗻𝘁𝘀 ↳ For each role or experience, identify specific tasks you performed and results you achieved. Ask yourself: What problems did I solve? How did I communicate? What did I manage or organize? 3/ 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝘇𝗲 𝘆𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀 ↳ Group them into categories like communication, leadership, problem-solving, technical abilities, project management, or analytical thinking. These broader categories usually apply across industries. 4/ 𝗠𝗮𝘁𝗰𝗵 𝘁𝗼 𝗷𝗼𝗯 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 ↳ Analyze relevant job postings and map them to the categories from step 3, even if you used them in different contexts. 5/ 𝗨𝘀𝗲 𝗰𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 ↳ When you identify a transferable skill, prepare specific stories that demonstrate it. Quantify your impact when possible - numbers make your experience more compelling. ♻️ Reshare this post for an aspiring career switcher and follow Megan Lieu for more!

The WSJ reports a seismic shift in tech hiring: entry-level roles have nearly vanished, hiring processes are lengthening, and employers now expect broader AI skills from applicants. Companies are delaying or canceling postings amid economic uncertainty and prioritizing candidates who can do more than just code—they must be able to collaborate with AI tools, think critically, and learn on the fly. What does this mean for professionals and HR? 1️⃣ Upskill with intention — Boost your AI fluency through bootcamps, certifications, or on-the-job experimentation like prompt engineering and tool orchestration. 2️⃣ Emphasize hybrid roles — Cultivate a mix of technical, critical thinking, and communication skills—you’re now a strategic integrator, not just a doer. 3️⃣ Be patient, be agile — The hiring market has entered a “Great Hesitation.” It’s competitive, yes—but proactive candidates with a future-forward skill set are still getting through. Tech careers might be tough to break into right now—but those who continuously adapt and demonstrate AI-augmented value will stand out. How are you reshaping your role or team for this new frontier? Read the article: https://lnkd.in/eXws8etX #FutureOfWork #TechCareers #AI #Upskilling #HiringTrends #TalentAcquisition #CareerDevelopment

According to the World Economic Forum, "technological change, geoeconomic fragmentation, economic uncertainty, demographic shifts and the green transition — individually and in combination — are among the major drivers that are expected to shape and transform the global labor market by 2030." [1] Tech changes: Broadening digital access is anticipated to be the most transformative trend, "with 60% of employers expecting it to transform their business by 2030". Advancements in technology (AI + information processing) are also expected to drive both the fastest-growing and fastest-declining roles, and fuel demand for technology-related skills. [2] Economic factors: Increasing cost of living is the second most transformative trend, with economic slowdown remaining top of mind. Slower job growth and mixed outlook for inflation will likely drive an increase in demand for creative thinking and resilience, flexibility, and agility skills. [3] Green transition: Climate-change mitigation is the third-most transformative trend overall, driving demand for roles such as renewable energy engineers, environmental engineers, and electric and autonomous vehicle specialists. [4] Demographic shifts: Perhaps the most interesting trend of all (for me, at least), is the one around demographic shifts. Aging and declining working age populations in higher-income economies and expanding working age populations in lower-income economies are reshaping the labor markets. Aging populations will likely drive growth in healthcare jobs while growing working-age populations will fuel demand for educators. [5] Geopolitical dynamics: Geoeconomic fragmentation and geopolitical tensions are expected to drive changes in the operations of businesses, including offsohring and reshoring. Also a few interesting items to note in the report: * Technology-related roles are the fastest growing jobs (in percentage terms), including "Big Data Specialists, Fintech Engineers, AI and Machine Learning Specialists". Meanwhile, Clerical and Secretarial Workers are expected to see the largest decline (in absolute numbers). ** Due to change in demand for skillsets, the need to upskill and reskill workforce is urgent. According to the WEF report, "if the world’s workforce was made up of 100 people, 59 would need training by 2030". This is significant. It is no wonder that 63% of employers identify skills gap as a major barrier to business transformation in the next five years. #AI #Fintech #FinancialServices #FutureOfWork #BankingOnAI

Friends, tonight, I want to address two common concerns that many of you have shared with me, concerns often seen as career setbacks. However, I believe these are not just positive, but pivotal steps in our professional journeys. 1. Diverse Roles Across Multiple Domains: the first point is about trying different roles, whether that's across organizations, divisions, functions, or even industries. It's a widespread belief that this indicates instability or lack of focus. I beg to differ. 2. Being in a Less-than-ideal Role: the second is about finding oneself in a role or function that doesn't feel quite right. It's easy to view this as a misstep. Yet, I see it as a valuable learning experience and a gift. In the early stages of our careers, everything is about selection and exploring possibilities. You could be anything - a fireman, a teacher, a dentist, an actor, and more. But as time progresses, our journey shifts from selection to elimination. Each role or industry we try and move on from helps us understand what we don't enjoy or want to pursue, narrowing our focus and making our path clearer. For many seasoned professionals, including myself, this process of elimination has led us to be able now to dismiss a vast array of opportunities outright, as we've learned they don't align with our goals or interests. My own experiences across various industries, like investment banking, taught me what wasn't right for me, helping me to refine my path. This approach is particularly crucial for those in college or starting their careers. Don’t limit your options too soon. Explore different courses, try varied jobs. How else will you discover what truly resonates with you? There might be a role you'll love that you haven't even considered yet. To those feeling stuck in a job that doesn't seem fulfilling, remember Thomas Edison's approach with the light bulb. Each experience is a step toward eliminating what doesn’t work for you, bringing you closer to that role where you'll thrive. Don't fear the perception of being a 'job hopper' or not sticking to one path. The role you'll love most might just be around the next corner. Your career is a journey of exploration, and every step, whether it feels right or wrong at the time, is a valuable part of that journey. Let's embrace the twists and turns of our professional paths. They aren't just inevitable; they are essential.

The role of product management, especially for AI-based products, is changing a lot. Interestingly, a significant number of products are becoming "AI-based" products. You'll often see requests for a stronger technical background alongside traditional PM skills. It's not enough to just know the market and users anymore; product managers now need to understand things like algorithms, data pipelines, and machine learning. This isn't a small change; it's a real shift in what's required. It’s not about knowledge of a toll but the technology. I'm seeing this trend firsthand. Look at product manager job descriptions, and "understanding or working knowledge of AI" is becoming standard. We're also seeing more data scientists and AI engineers moving into product management. This isn't just a career switch; it's a sign that technical knowledge is crucial for building good AI products. For people without this background, it's a big challenge, requiring a lot of learning and a willingness to try new things. Being able to explain complex technical ideas in a way that users understand is now a must-have skill. The key to AI product management is balancing big ideas with what's actually possible. Without understanding AI's strengths and limitations, product managers can easily get swayed by marketing hype or struggle to create realistic roadmaps. It's the difference between a dream and a practical vision. Equally important is building strong communication with engineering teams, not just for technical alignment but for building trust. Don't believe the idea that you don't need technical skills in PM. This trend is only going to get stronger. It's better to adapt and learn than to struggle later. #ExperienceFromTheField #WrittenByHuman

Netflix pays AI PMs up to $900K/year. How do you become (and succeed) as one? Marily Nika, Ph.D is an AI PM at Google, and was an AI PM at Meta. In today's episode, she breaks it down: - How AI PM Roles Differ at Big Tech Companies - Interviewing for AI PM Roles at Big Tech Companies - Transitioning into AI Product Management - How PMs Can Stay Relevant in an AI-Driven World - LinkedIn Tips for Aspiring AI PM Creators — So don't miss it: YouTube: https://lnkd.in/eVqyTAgv Spotify: https://lnkd.in/eyt7agKj Apple: https://lnkd.in/eKEAa4wj — And check out our awesome sponsors: 1. Vanta: The best tool to automate compliance, manage risk, and prove trust http://vanta.com/aakash 2. Maven: I’ve just launched my unique curation of their top courses http://maven.com/x/aakash — Here were my favorite takeaways: 1. AI PM roles can be categorized as: AI Builder PMs → They work closely with researchers to train, test, and improve AI models. AI Enhanced PMs → They take those models and turn them into real-world applications that users actually interact with. — 2. The fastest way to fail as an AI Builder PM? Walk into a meeting with a research team and say, "I have an idea! Can you build it?" Here’s what the best AI PMs do differently: They master technical influence → Engineers respect PMs who bring structure, clarity, and product thinking to AI. They know AI’s unique challenges → AI is probabilistic, not deterministic; results will never be perfect. They speak their language → Ask precise questions like; “What’s our model’s precision and recall? “Do we need more training data?” — 3. To get an AI PM job, you have to make yourself impossible to ignore. A. Build something of your own — something to showcase your expertise. It doesn’t have to be a fully planned product. B. Build a strong online presence so hiring managers can find you easily. C. Don’t just apply — get referrals through cold emails, networking, and personal connections D. Treat interviews like a performance and practice until your answers flow naturally. Check out the episode for more.