Career

Does becoming a parent affect academic trajectories? Yes, and particularly so for women: 🔹University employment drops by almost 30 percent for women, much less so for men. 🔹Probability of being tenured is about 35 percent lower for women, no change for men. 🔹Number of publications is 31 to 35 percent lower for women, no change for men. These are among the key results of a new study by Sofie Cairo, Ria Ivandic, Anne Sophie Lassen, and Valentina Tartari using rich Danish admin data. Why do women’s and men’s career trajectories diverge sharply after parenthood? The paper studies a series of candidate explanations and finds that the gap in childcare load seems mainly responsible: "women academics do almost five times more childcare than their male academic counterparts. For example, 55% of female researchers get up at night to take care of the child most or all the time, in comparison to 11% of men". Read the full paper here: Sofie Cairo, Ria Ivandic, Anne Sophie Lassen, and Valentina Tartari (2026), Parenthood and the career ladder: evidence from academia, Centre for Economic Performance discussion paper: https://lnkd.in/eK2gWzSb

My candidate landed a ₹15 LPA offer at a top MNC without even applying. No resume drop. No job portal. How? ✅ She unlocked the hidden job market that most candidates never see. So, how did she do it? Not with luck. But with a strategy anyone can use: 1. She built her brand before she needed a job. She shared her wins, projects, and insights on LinkedIn consistently. Example: Every Friday, she posted a carousel breaking down a real-life analytics problem she solved at work, tagging teammates and sharing key takeaways. This made her visible as a problem-solver in her field. 2. She reached out to industry peers, not just HR. No generic “Hi, can you refer me?” Instead, she started real conversations about trends, challenges, and solutions in her field. Example: She messaged a data scientist at her dream company, commenting on a recent paper he’d published: 👇 “Hi Raj, I loved your article on predictive analytics in retail. I’ve been working on similar models for FMCG clients and would love to exchange notes!” This led to a meaningful chat, not a cold request. 3. She gave before she asked. She offered feedback on others’ work, shared resources, and celebrated others’ milestones. Example: She congratulated connections on promotions, shared helpful webinars in group chats, and offered to review a peer’s resume before asking for any help herself. 4. She followed up, politely and persistently. After every conversation, she sent a thank-you note: 👇 “Thanks for your insights, Priya! I’ve already started applying your advice. Hope we can catch up again soon.” She stayed top of mind, not just top of the inbox. You don’t need a massive network. You need genuine connections, a clear story, and the courage to show up before you need help. If you’re still waiting for the “perfect” job post to appear, you’re already late. The best opportunities are shared in DMs, whispered in meetings, and offered to those who are already visible. Start building your presence, your relationships, and your reputation today. #jobsearch #jobopportunities #jobinterview #careergrowth

I constantly get recruiter reachouts from big tech companies and top AI startups- even when I’m not actively job hunting or listed as “Open to Work.” That’s because over the years, I’ve consciously put in the effort to build a clear and consistent presence on LinkedIn- one that reflects what I do, what I care about, and the kind of work I want to be known for. And the best part? It’s something anyone can do- with the right strategy and a bit of consistency. If you’re tired of applying to dozens of jobs with no reply, here are 5 powerful LinkedIn upgrades that will make recruiters come to you: 1. Quietly activate “Open to Work” Even if you’re not searching, turning this on boosts your visibility in recruiter filters. → Turn it on under your profile → “Open to” → “Finding a new job” → Choose “Recruiters only” visibility → Specify target titles and locations clearly (e.g., “Machine Learning Engineer – Computer Vision, Remote”) Why it works: Recruiters rely on this filter to find passive yet qualified candidates. 2. Treat your headline like SEO + your elevator pitch Your headline is key real estate- use it to clearly communicate role, expertise, and value. Weak example: “Software Developer at XYZ Company” → Generic and not searchable. Strong example: “ML Engineer | Computer Vision for Autonomous Systems | PyTorch, TensorRT Specialist” → Role: ML Engineer → Niche: computer vision in autonomous systems → Tools: PyTorch, TensorRT This structure reflects best practices from experts who recommend combining role, specialization, technical skills, and context to stand out. 3. Upgrade your visuals to build trust → Use a crisp headshot: natural light, simple background, friendly expression → Add a banner that reinforces your brand: you working, speaking, or a tagline with tools/logos Why it works: Clean visuals increase profile views and instantly project credibility. 4. Rewrite your “About” section as a human story Skip the bullet list, tell a narrative in three parts: → Intro: “I’m an ML engineer specializing in computer vision models for autonomous systems.” → Expertise: “I build end‑to‑end pipelines using PyTorch and TensorRT, optimizing real‑time inference for edge deployment.” → Motivation: “I’m passionate about enabling safer autonomy through efficient vision AI, let’s connect if you’re building in that space.” Why it works: Authentic storytelling creates memorability and emotional resonance . 5. Be the advocate for your work Make your profile act like a portfolio, not just a resume. → Under each role, add 2–4 bullet points with measurable outcomes and tools (e.g., “Reduced inference latency by 35% using INT8 quantization in TensorRT”) → In the Featured section, highlight demos, whitepapers, GitHub repos, or tech talks Give yourself five intentional profile upgrades this week. Then sit back and watch recruiters start reaching you, even in today’s competitive market.

4 agency owners. 3 hours of raw conversation. Every secret about scaling from freelancing to 7-figures (from beginner to advanced level) Beginner Stage: -Don't wait to be 100% ready. Start with broken skills but improve FAST -Work for free initially, but be strategic - ask for referrals in return -Create a strong portfolio by doing real projects (even if unpaid) -Take every opportunity to learn and upskill -Money shouldn't be your only motivator - focus on skill development Intermediate Stage: -Start taking your finances seriously - get a CA, track cash flow -Invest money back into the business (I waited 3.5 years for an office - big mistake) -Build systems before you need them -Get comfortable with delegation -Hire people better than you at specific skills Advanced Stage: -Over-hire slightly - it's better than being understaffed -Focus on building a strong team culture -Make your team feel valued beyond just salary -Invest in proper infrastructure (legal, accounting, tools) -Remember: Take care of your team, and they'll take care of your clients To sum it up: In the beginning, you focus on yourself. In the intermediate stage, you focus on the business. But in the advanced stage - you learn that true growth comes from focusing on your team's growth. The moment I switched from "How do I grow?" to "How do I help my team grow?" - everything changed. We shared all this in more detail in the podcast, link in the comments.

After spending three decades in the aerospace industry, I’ve seen firsthand how crucial it is for different sectors to learn from each other. We no longer can afford to stay stuck in our own bubbles. Take the aerospace industry, for example. They’ve been looking at how car manufacturers automate their factories to improve their own processes. And those racing teams? Their ability to prototype quickly and develop at a breakneck pace is something we can all learn from to speed up our product development. It’s all about breaking down those silos and embracing new ideas from wherever we can find them. When I was leading the Scorpion Jet program, our rapid development – less than two years to develop a new aircraft – caught the attention of a company known for razors and electric shavers. They reached out to us, intrigued by our ability to iterate so quickly, telling me "you developed a new jet faster than we can develop new razors..." They wanted to learn how we managed to streamline our processes. It was quite an unexpected and fascinating experience that underscored the value of looking beyond one’s own industry can lead to significant improvements and efficiencies, even in fields as seemingly unrelated as aerospace and consumer electronics. In today’s fast-paced world, it’s more important than ever for industries to break out of their silos and look to other sectors for fresh ideas and processes. This kind of cross-industry learning not only fosters innovation but also helps stay competitive in a rapidly changing market. For instance, the aerospace industry has been taking cues from car manufacturers to improve factory automation. And the automotive companies are adopting aerospace processes for systems engineering. Meanwhile, both sectors are picking up tips from tech giants like Apple and Google to boost their electronics and software development. And at Siemens, we partner with racing teams. Why? Because their knack for rapid prototyping and fast-paced development is something we can all learn from to speed up our product development cycles. This cross-pollination of ideas is crucial as industries evolve and integrate more advanced technologies. By exploring best practices from other industries, companies can find innovative new ways to improve their processes and products. After all, how can someone think outside the box, if they are only looking in the box? If you are interested in learning more, I suggest checking out this article by my colleagues Todd Tuthill and Nand Kochhar where they take a closer look at how cross-industry learning are key to developing advanced air mobility solutions. https://lnkd.in/dK3U6pJf

I have made Over 1 CR as a Freelancer. Years ago, I was struggling to find clients, sending out pitch after pitch with no success. After trial and error, I discovered the strategies that turned my freelancing journey into a 7-figure success story. Today, I'm sharing my top pitching techniques with you. ✅ Strategy 1: Comment Strategy How to Use: Engage with top creators on LinkedIn, DM them, share resources, nurture relationships, then pitch. Benefit: Builds strong relationships and trust. ✅ Strategy 2: Video Pitches How to Use: Create personalized video pitches. Benefit: Personalization increases engagement. ✅ Strategy 3: Value Ladder Offers How to Use: Start with a low-commitment offer like a free audit. Benefit: Eases clients into your services. ✅ Strategy 4: Exclusive Insights How to Use: Offer exclusive insights or industry reports. Benefit: Demonstrates expertise and adds value. ✅ Strategy 5: Success Stories Follow-Up How to Use: Follow up with a success story from a similar client. Benefit: Provides social proof. ✅ Strategy 6: Free Tools or Templates How to Use: Share free tools or templates, then pitch comprehensive services. Benefit: Demonstrates value and expertise. ✅ Strategy 7: Social Proof Landing Pages How to Use: Direct clients to a landing page with testimonials and case studies. Benefit: Builds credibility and trust. ✅ Strategy 8: Follow-Up with Added Value How to Use: Follow up with additional valuable content related to the client’s business. Benefit: Keeps you top-of-mind and adds value. ✅ Strategy 9: Personalized Case Studies How to Use: Create case studies tailored to your potential client’s industry. Benefit: Shows clients how you can solve their specific problems. ✅ Strategy 10: Niche-Specific Content How to Use: Develop content highly relevant to the niche of your potential client. Benefit: Positions you as an expert in their industry. ✅ Strategy 11: Client Education How to Use: Educate clients on industry trends and solutions before pitching. Benefit: Builds trust and positions you as a knowledgeable resource. I've excelled at pitching potential clients and succeeded in sealing 99% of deals to date. I've taught my 5000+ students all the secret strategies of getting high-paying clients, and today, I see them making 50K-1Lac a month easily. 📌 If you're interested in learning from me & my 6-figure team, DM 'Freelance' for details. Question: Do you find it helpful?

As a junior lawyer, I got copied into client email threads and didn’t know what I should do besides waiting to be delegated tasks. Here are 5 things that I’ve learned to do to be more involved: 1️⃣ Project management Extract actions and proposed due dates from client correspondence. Bonus points if you have a project tracker that you can continually update. “Hi Jane, I’ve read through the 6 emails sent by [client] today and set out the action items below. I’ll keep updating this list as the day goes on.” 2️⃣ File management Save all correspondence and documents to the client / matter file. “Hi Jane, just letting you know I’ve saved these documents to [system]. Attached is the link to the [document] if you’d like to review it now.” 3️⃣ Offer to do the first draft of the document / task / email response “Hi Jane, [client] has asked us to amend the contract to reflect the agreed issues list by Friday (4 August). I can prepare a first draft for your review by Thursday morning if you’d like.” 4️⃣ Get familiar with the client Take note of their communication style, key contacts, approvals process, concerns, business drivers, preferred forms of documents, billing procedures, upload portals etc. Being aware of these things helps with providing a better personalised legal service. 5️⃣ Handle routine inquiries Simple and routine inquiries from clients can be handled by junior lawyers, freeing up the senior lawyer's time for more complex and strategic matters. Check with your supervisor about the types of things they would like you to handle. For those tasks, if not you’re not sure about your response, prepare a draft email for their review. “Hi Jane, I’ve prepared a draft response to [client] below but was not 100% sure about a few things. I’ve flagged my questions and proposed responses but would appreciate it if you could run your eye over it before we send it out.” Are these things that you do? Anything else you’d add? ------------- Btw, if you're a junior lawyer looking for practical career advice - check out the free how-to guides on my website. You can also stay updated by sending a connection / follow. #lawyers #lawstudents #legalprofession #lawschool

I went from PhD to lectureship.  Everyone said congratulations.  No one told me what was coming. The title changed. But behind it... I was drowning. Not because I wasn't capable. But because I wasn't prepared for what was actually waiting on the other side. So if you're a PhD student thinking of staying in academia, hear me out. 𝟭. 𝗞𝗻𝗼𝘄 𝘄𝗵𝗮𝘁'𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗰𝗼𝗺𝗶𝗻𝗴. Academia isn't just teaching and research. It's course design, exam setting, marking, committees, paper reviews, endless emails, supervision at every level, admin, advising, progression reviews, examiner duties, public engagement, grant applications, paper publications, impact generation... Some days I switch between 5 tasks before lunch. BTW, I also had to take a compulsory 2-year part-time course (postgraduate certificate of academic practice) when I started... sitting in lectures and submitting coursework on top of everything else. If you don't see it coming, it will flatten you. --- 𝟮. 𝗙𝗶𝗻𝗱 𝘆𝗼𝘂𝗿 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗼𝗿𝘀 𝗻𝗼𝘄. I thought I'd have time to do research like I did during my PhD. But that vanished immediately. When I asked others how they manage,  they said: collaborators, research students, postdocs. But I started during the pandemic. No conferences. No networking.  No way to meet people. And when things opened up again... I was already drowning in tasks. Trying to build relationships from scratch while the clock was ticking felt impossible. Start now during your PhD.  Find your people before you desperately need them. --- 𝟯. 𝗟𝗲𝗮𝗿𝗻 𝗮𝗯𝗼𝘂𝘁 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗳𝗲𝗹𝗹𝗼𝘄𝘀𝗵𝗶𝗽𝘀 𝗲𝗮𝗿𝗹𝘆. I didn't even fully understand what a fellowship was until I was already overwhelmed. It pays your salary and protects your time for research. No teaching overload. No drowning in admin. If research is your priority, start exploring these before you even finish your PhD. Don't wait until you're gasping for air. --- The jump from PhD to academia doesn't have to break you. But only if you prepare while you still have time. What's ONE thing you wish you knew before starting your academic career?

You're in a job interview, you get the offer—but the salary? Way lower than expected. The worst move? Accepting on the spot. The second worst? Declining outright. Here's how you can take the 'ick' out of negotiating: 1. Start with Gratitude →“Thank you for the offer.” 2. Share Excitement →“I’m really excited about the role and joining the company.” 3. Address the Salary →“Before I accept, I’d like to discuss the salary. It’s below what I believe reflects the market value for my experience.” 4. Reinforce Your Value →“I’m confident my expertise in A and B, and my contributions to C and D will drive success here.” 5. Reiterate Market Value →“Based on my research and track record, I believe a salary range of X to Y would be more in line with the industry.” Where to do research? Check salary data on sites like Glassdoor, Payscale, and LinkedIn, or ask industry peers and recruiters for real-world insights. Pro tip: Use multiple sources to get a well-rounded view and always adjust for location and years of experience. P.S. Have you ever accepted a salary because you didn't know how to negotiation? I'll go first: Yes, I have...

Data Integration Revolution: ETL, ELT, Reverse ETL, and the AI Paradigm Shift In recents years, we've witnessed a seismic shift in how we handle data integration. Let's break down this evolution and explore where AI is taking us: 1. ETL: The Reliable Workhorse      Extract, Transform, Load - the backbone of data integration for decades. Why it's still relevant: • Critical for complex transformations and data cleansing • Essential for compliance (GDPR, CCPA) - scrubbing sensitive data pre-warehouse • Often the go-to for legacy system integration 2. ELT: The Cloud-Era Innovator Extract, Load, Transform - born from the cloud revolution. Key advantages: • Preserves data granularity - transform only what you need, when you need it • Leverages cheap cloud storage and powerful cloud compute • Enables agile analytics - transform data on-the-fly for various use cases Personal experience: Migrating a financial services data pipeline from ETL to ELT cut processing time by 60% and opened up new analytics possibilities. 3. Reverse ETL: The Insights Activator The missing link in many data strategies. Why it's game-changing: • Operationalizes data insights - pushes warehouse data to front-line tools • Enables data democracy - right data, right place, right time • Closes the analytics loop - from raw data to actionable intelligence Use case: E-commerce company using Reverse ETL to sync customer segments from their data warehouse directly to their marketing platforms, supercharging personalization. 4. AI: The Force Multiplier AI isn't just enhancing these processes; it's redefining them: • Automated data discovery and mapping • Intelligent data quality management and anomaly detection • Self-optimizing data pipelines • Predictive maintenance and capacity planning Emerging trend: AI-driven data fabric architectures that dynamically integrate and manage data across complex environments. The Pragmatic Approach: In reality, most organizations need a mix of these approaches. The key is knowing when to use each: • ETL for sensitive data and complex transformations • ELT for large-scale, cloud-based analytics • Reverse ETL for activating insights in operational systems AI should be seen as an enabler across all these processes, not a replacement. Looking Ahead: The future of data integration lies in seamless, AI-driven orchestration of these techniques, creating a unified data fabric that adapts to business needs in real-time. How are you balancing these approaches in your data stack? What challenges are you facing in adopting AI-driven data integration?