Shework.in

Everyone obsesses over how slow hiring is. Almost no one prices what a fast, wrong hire actually costs. Here's the math that reframes the whole debate. A mis-hire runs anywhere up to 30% of their annual salary once you add it all up. The recruitment redo, the onboarding sunk cost, the manager's hours, the team drag while a wrong-fit person occupies a critical seat, and the quiet morale tax nobody invoices. Speed feels productive. Precision is what's actually cheap. This isn't an argument for slow, bloated processes — those lose good candidates too. Which mistake does your team make more often — too slow, or too sure? #CostOfHiring #QualityOfHire #SheWork #TalentAcquisition #HiringData

The first half of 2026 is onto us already. Here's where the hiring demand is genuinely moving as we head into H2. The headline isn't 'AI, everything, everywhere.' It's more specific and more useful than that. #Cybersecurity and cloud security are pulling ahead as every AI deployment creates new attack surface — demand here is outrunning the trained supply badly. #Data engineering keeps climbing, because every company's AI ambition quietly rests on a data foundation most of them haven't built. #Green and sustainability roles are heating up from a small base, with the degree premium still intact. And #AI governance — the ethics, compliance, and DPDP-aligned oversight layer — went from a footnote to a hiring line in a single year. What's cooling: generic 'AI-curious' roles with no depth, and pure-screening junior tasks that agents now absorb. The pattern for H2: hire for depth in narrow, foundational skills. Breadth is getting automated. What's your H2 hiring priority? Comment below. #H2Hiring #TechTrends #SheWork #TalentAcquisition #IndiaHiring

Your best candidate accepted. Then their 90-day notice period turned a win into a waiting game. India's long notice periods are one of the most under-discussed frictions in hiring here. Three months is an eternity in a market where roles get backfilled, budgets shift, and second-choice candidates get snapped up. By the time the joiner is free, half the context that made the role urgent has changed. What actually works: 1. Negotiate a buyout early, not in week eleven. Many companies will offset it; almost none offer unless asked. 2. Stay in real contact through the wait. A candidate on a 90-day countdown is a candidate three months exposed to counter-offers. 3. Build a bench, not a single bet. If your whole plan rests on one joiner ninety days out, you don't have a plan, you have a hope. The notice period isn't going away. Pretending it doesn't shape your hiring is the mistake. How does your team handle the 90-day gap? Comment below. #NoticePeriod #TechHiring #SheWork #TalentAcquisition #IndiaHiring

You found the right person. But is it like work done? Actually, it's where the part begins that decides whether the hire worked. We close roles, but the number we answer to isn't the placement — it's the 90-day mark. Is the person still there? Still doing well? In a market where 1 in 4 offers never even becomes a join, and where new hires drift in the first two weeks, the close is the easy part. Here's the thing most teams miss: retention doesn't begin at the six-month review. It begins at the offer call — in how honestly you describe the role's real challenges, how deliberately you design the first week, how present the manager is in the first month. The hires that stick aren't lucky. They land somewhere that was as prepared for them as they were for the role. Here's to the hires that stay. #TalentRetention #HiringTruth #SheWork #PeopleFirst #TalentAcquisition

Every company claims to hire for potential. Then runs an interview built entirely around the past. It matters more in 2026: the job itself now changes every quarter. AI tooling, new stacks, shifting scope. Experience is a snapshot of a role that may not exist by the time someone's ramped. Potential — the capacity to absorb a moving target — is the only thing that stays relevant. The problem is we interview for the wrong signals. 'She seemed hungry' isn't potential. 'Great energy' isn't potential. Those are vibes, and vibes correlate with confidence, not capability. Made concrete, potential is three things: #LearningVelocity: how fast did they get useful in an unfamiliar context? #PatternRecognition: can they see what's really happening #SetbackResponse: what did they actually do the last time something broke? All three are interviewable. None require ten years of experience — which is exactly why they widen your pool instead of shrinking it. #HiringForPotential #FutureOfWork #SheWork #InterviewTips #SkillsFirstHiring

Here's how our best placement of the year actually happened: a simple text message that said, 'I know someone you should talk to." Not an application. Not an AI-ranked shortlist. A referral from someone we'd placed two years earlier, who remembered what it felt like to be found the right way and passed it forward. She was a Data Engineering lead. Not looking. The role was never posted publicly — so no scraper, no parser, no agent would ever have surfaced her. She joined in 19 days. Still there. This is the part of hiring that doesn't automate, and we don't think it ever fully will. Trust compounds. Every person placed well becomes a node that eventually finds someone else the right opening. A community isn't a database you query. It's a network that remembers. The faster the tools get, the more valuable the human graph becomes. #CommunityHiring #ReferralHiring #SheWork #WomenInData #TalentAcquisition

On average, your LinkedIn profile gets two auditions. A recruiter spends about 11 seconds on it. An AI parser spends a fraction of one — and increasingly, it screens first. Most advice optimises for one and ignores the other. You need both. The AI reads your headline as keywords, your skills as tags, your bullets as parseable data. Vague poetry confuses it. 'Driving synergies across verticals' is invisible to a parser. 'Backend engineer | Go, Kafka, AWS | scaled payments to 2M users' is legible to the machine and compelling to the human. The human, eleven seconds later, reads the same profile as positioning and proof. Same words, different job. The profiles that win both have a headline that states what you do and for whom, an About section that earns its read in three sentences, and bullets built on impact rather than inventory. #LinkedInTips #JobSearch #AIinHiring #SheWork #CareerTips

"Not the right culture fit" - the most-used rejection reason in hiring — and the least examined. Sometimes it's real and worth naming: a high-autonomy person dropped into a highly structured team genuinely won't thrive. That's a useful signal. But far more often, 'culture fit' is a socially acceptable container for 'not like the people we already have.' And in 2026, with AI screening already inheriting old biases at the top of the funnel, an unexamined 'fit' call at the bottom just compounds it — bias at intake, bias at decision, with a polite phrase covering both. The teams that hire well define fit in concrete, testable terms before the first interview. #CultureFit #BiasInHiring #SheWork #DEI #HiringTruth

𝗦𝗼𝗺𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀 𝗰𝗿𝗲𝗮𝘁𝗲 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀. 𝗢𝘁𝗵𝗲𝗿𝘀 𝗰𝗿𝗲𝗮𝘁𝗲 𝗶𝗺𝗽𝗮𝗰𝘁. LEAP East 2026 gave us the opportunity to connect with leaders, innovators, enterprises, and changemakers who are shaping the future of technology and talent. For Team Shework.in, these three days were filled with meaningful discussions around inclusive hiring, workforce transformation, women in tech, and the evolving talent landscape. We're grateful to everyone who visited our booth, exchanged ideas, and shared their vision for the future. Here's a look back at an inspiring journey. 𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝗮𝗵𝗲𝗮𝗱. #LeapEast2026 #SheWork #WomenInTech #FutureOfWork #InclusiveHiring #GlobalNetworking Pankaj Deshmukh Ramya Athreya Manish Bhardwaj

"𝗧𝗵𝗲 𝘄𝗶𝗻𝗻𝗲𝗿𝘀 𝘄𝗼𝗻'𝘁 𝗯𝗲 𝘁𝗵𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜." A line that reframes the entire conversation. Bharti Sharma, Senior Director – Enterprise Transformation, Service Management & Platform Strategy at #Aon, on why judgment, problem-solving, and human potential still hold the edge, and why she sees this moment as a genuine opportunity for women in tech. Watch the full conversation here: https://lnkd.in/g9NPqYTK #WomenEverydaySeries #SheWork #ServiceNow #AIatWork #FutureOfWork #WomenInTech