Hiring great talent has never been easy, but in today’s world of exploding data stacks, AI disruption, and nonstop talent wars, it’s starting to feel like rocket science. Every company wants top data talent, yet few can actually find it.

Inside boardrooms and data teams, the same story plays out again and again: projects stall because the right data engineer isn’t in place, dashboards stay half-built, and innovation takes a back seat to survival.

That’s the problem Ian Allison set out to solve when he founded Salient Insights in late 2021. His mission was simple yet ambitious – help organizations find “A-players” for their data and analytics teams, fast.

Allison specializes in value-driven analytics, data governance, AI enablement, and leadership alignment, and draws on his experience as Chief Data Officer (CDO) and Principal Data Architect at one of Microsoft’s larger government projects.

“When you’re in a leadership role, your success is dependent on the quality of your team. The difference between A-players and B-players is enormous. A-players don’t just meet goals; they transform outcomes,” he says.

In a world where 76% of employers report talent shortages in 2025, and where “finding qualified candidates” remains a persistent top-three recruiting challenge, Salient Insights aims to offer an alternative to the resume-screening treadmill.

For organizations building data teams, the promise is clear: fewer mis-hires, shorter time-to-fill, and access to a curated pool of high-impact talent.

“For leaders frustrated by staffing delays or weak hires in data and analytics, this kind of specialized, technically-driven hiring model may just be the competitive edge they need.”

Tackling the talent gap

At its heart, Salient Insights is designed to bring technical depth to the hiring process. Traditional recruitment agencies, Allison argues, often lack the expertise to properly evaluate data science and engineering candidates.

“We connect companies with true experts, people who are already leaders in tools like Databricks, Snowflake, or PyTorch, and have them conduct the interviews,” he says. 

Allison describes this model as “an Uber for talent” — a dynamic marketplace matching demand (companies seeking data talent) with supply (experts who conduct the evaluations). The expert interviewers are compensated on a commission basis and are drawn from the highest tier of technical professionals.

“The experts get compensated for their time, and clients get a highly accurate evaluation,” Allison explains. Clients begin by engaging Salient through a typical staffing-vendor arrangement. The company collaborates with the client to define the role, the tools required, the roadmap, and the metrics.

Allison outlines the process: “We create the job posting and proactively reach out to passive candidates. Every applicant completes a one-way recorded pre-screen interview, which we score and filter to identify the top ten. A subject matter expert then conducts detailed technical interviews, and we share the recordings and summaries with the hiring manager, who does the final culture-fit interview. In most cases, our clients find the right candidate within the first or second round.”

By moving much of the technical debt away from the hiring manager and into the expert-interviewer model, Salient Insights aims to radically compress time-to-hire while improving candidate quality- two metrics that are solely needed.

Leveraging AI

Allison also says his startup leverages AI at multiple levels in the whole interview process. AI tools are used for one-way interview transcriptions and scoring. Salient leverages a combination of third-party tools as well as internally developed models. Its proprietary models are specifically designed to account for speech impediments, ensuring that no individual is unfairly disadvantaged.

“What we do, without giving away too much of the secret sauce, is ensure that every candidate gets an interview,” says Allison. “I’ve found that some of the best candidates often have the worst résumés because they’ve moved through roles via networks and never needed to polish them. So, we give everyone a one-way recorded interview, which our AI system then scores.”

The company also runs back-tests to ensure the AI system remains fair and unbiased, preventing it from disadvantaging candidates with accents or speech patterns. This approach helps guarantee that every applicant receives an equal opportunity to succeed.

“For example, someone might have an accent or speech pattern that affects automated transcription. Our models are designed to correct for that, so we don’t disadvantage anyone unfairly. AI helps us maintain fairness and consistency while filtering large candidate pools efficiently,” he says.

Why this model matters — and what’s next

What makes Salient Insights particularly relevant in today’s market is the depth of the skills gap in the data domain. As noted above, the shortage of data engineering skills is acute, and companies are increasingly prioritising specialists over generalists.  Allison emphasises that Salient Insights is deliberately focused on the data space.

“We stay tightly focused on the data space — data analytics, data engineering, data governance, data science, business intelligence. If ‘data’ is in the title, we probably cover it.”

The client base tends toward mid- to large-cap enterprises — companies with revenues of roughly $500 million to over $1 billion. Allison points out that while smaller organisations benefit greatly from the hire of a high-performer, they often lack budgets for external recruiters.

Nonetheless, one of their success stories included a client who had been searching for months for a key data role; after Salient Insights submitted one candidate, the client extended an offer within three hours, and then asked the firm to bring in ten consultants and three full-time hires.

According to Allison, no other companies have managed to bring this kind of sophistication to recruitment like his startup. 

“Some firms claim to do it, but none really execute it the way we do. Larger consultancies like Deloitte or McKinsey may say they use internal experts for screening, but in reality, it’s often just resume reviews or AI-driven filters. We go several steps further — our experts perform real technical interviews, just as they would if they were hiring for their own teams.”

Looking ahead, the company plans to expand beyond data roles into adjacent technology disciplines. “The next step is scaling this model into new tech domains, areas like AI engineering, cybersecurity, software development, but only if we can maintain the same standard of quality,” says Allison. 

“If we can bring A-players on board, the whole thing transforms — not just meeting goals, but exceeding them.”