The Hiring Market Is Changing Fast: How to Stand Out in 2026

Suneetha Malkani

Oct 29 2026

<div class='bc_element' id='bc_element1' style='width:auto;padding:5px;max-height:100%;'><span><p class="no-margin startPlaceholder">“The U.S. job market isn’t in a recession, but for workers seeking new opportunities, it might feel that way.” (Source: <a href='https://www.forbes.com/sites/courtney-connley-hampton/2026/10/02/the-job-market-is-barely-moving-and-workers-are-paying-the-price/?utm_source=https://tote.work'>Forbes</a>) Ask a recruiter and you may hear something different. I recently spoke with <a href='https://www.linkedin.com/in/ois%C3%ADn-ennis-098a9a12a/?utm_source=https://tote.work'>Oisín Ennis</a>, who leads a recruiting team at <a href='https://www.linkedin.com/company/harnham/home/?utm_source=https://tote.work'>Harnham</a> in New York, specializing in data and analytics talent. His read on the market, plus what he sees every day across candidates and hiring managers, points to a handful of shifts worth understanding. <b>Hiring is active. <u><b>Selectivity</b></u> has risen. </b> According to Oisin, hiring has been steady over the last 12 to 18 months. What has changed is how specific companies have become about who they will hire. Being technically qualified used to carry a candidate a long way. Today, hiring managers look closely at the environment you worked in, how much ownership you had, how you handled ambiguity, and what your work did for the business. The result is a gap in perception. A candidate looks at their experience and reasonably concludes they fit the role. The company is measuring against a much narrower definition of what good looks like. <b>Domain experience matters more in some industries </b> Some sectors hold a firm line on background. Health tech is a clear example, especially for roles involving clinical data. Finance, including healthcare finance and the fast-growing credit space, follows close behind. In marketing, employers often want to see B2B or direct-to-consumer experience, and CPG draws similar attention. Other industries show more flexibility. Companies building foundational teams tend to weigh technical skill and communication ability above industry pedigree. Retail has been reimagined several times over the past decade and sees plenty of engagement, and Oisin noted that much of the new activity in SaaS involves reinventing older business models with AI. <b>Titles are shifting while the work stays the same </b> The same job can carry different names at different companies. “Data scientist” is among the most ambiguous titles in the field, and recruiters at different agencies often describe identical roles in different ways. Oisin starts every search with a conversation with the hiring manager to understand the real work, then makes the case for flexibility on titles. Candidates carry part of this responsibility. If your title says data analyst and your work includes data science, your LinkedIn profile needs to show that work. A recruiter filling a data science role will give a lower priority to a profile with no evidence behind the claim. <b>The application process is getting noisier </b> Automation has changed the volume side of hiring. Oisin described posting a job and receiving around 200 applications by the next morning. Some come from people who never read the posting, such as applicants from Florida for a New York hybrid role. Some may come from fake candidates or bots, which adds a layer of identity validation to many searches. For recruiters, AI has become a strong administrative assistant. It summarizes client briefs, sharpens job descriptions, and supports market mapping. Oisin was clear about the boundary: the first engagement with a candidate should be a person, and relationship-building should stay human. <b>AI-polished resumes are making everyone look alike </b> It is easy now to produce a well-written resume tailored to a job description, and to rehearse polished interview answers. None of that is harmful. The side effect is that capability becomes harder to tell apart, because many applications read the same way. What AI cannot supply is lived experience. Oisin pointed to analytics and data science as fields where the job is to deliver insight. Candidates who show how they thought through a problem tend to land well: what they identified, why they chose their approach, how they executed, and what resulted. Listing the tools you used tells a hiring manager much less than describing the path you took. <b>What to watch over the next 12 months </b> <b>Oisin highlighted three trends:</b> i) Specialization. Data teams are splitting into distinct functions: data science, analytics, analytics engineering, and data engineering. Titles are catching up more slowly than the org charts. ii) Location policies. More organizations are firm on hybrid requirements, which materially changes the candidate pool. iii) AI automation. Companies are deciding which work to automate and which to keep human, and that will reshape departments. <b>What this means for you </b><br></p><p class="no-margin startPlaceholder"><b style='display: inline-block; margin-bottom: 20px;'>Job seekers and career changers</b> i) Show tangible proof. Side projects, portfolios, and extracurricular work give hiring managers something to see. Oisin noted that trust comes easier when there is evidence behind the pitch. ii) Expect to compete with candidates who hold the exact commercial experience. Your pivot story needs concrete support. iii) Read postings carefully. Apply where location and requirements match. <b style='display: inline-block; margin-bottom: 20px;'>Early-career candidates</b> i) Ambition is the strongest signal. Oisin said enthusiasm for tough challenges and a drive to do meaningful work matter alongside skills. ii) Pair technical ability with curiosity about how the business uses your work. <b style='display: inline-block; margin-bottom: 20px;'>Hiring managers and HR leaders</b> i) Spell out the real work in the job description. Flexible titles widen your pool without lowering the bar. ii) Brief your recruiters early and in detail. A day-one conversation about the actual need saves weeks later. iii) Keep first contact with candidates human, and use AI for the paperwork. <b style='display: inline-block; margin-bottom: 20px;'>Data and analytics professionals</b> i) Write your resume as a story of decisions: the problem, your reasoning, your method, the result. ii) Mirror the function you want in your LinkedIn profile, whatever your current title says. iii) Combine analytical depth with business sense. Oisin sees the strongest candidates merging both mindsets. <b>The takeaway </b> The market rewards specificity and evidence. Candidates who show how they think, back their claims with visible work, and make the human connection stand out in a crowded field. Thank you to <a href='https://www.linkedin.com/in/ois%C3%ADn-ennis-098a9a12a/?utm_source=https://tote.work'>Oisín Ennis</a> of <a href='https://www.linkedin.com/company/harnham/home/?utm_source=https://tote.work'>Harnham</a> for sharing his perspective. </p> <span></div>

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