Recruit and Hire Machine Learning Talent With HopHR

At Hop, we connect top machine learning talent with companies that need it most. Here, we explore the ML spectrum and its distinguishing skills and technologies.

Hire Machine Learning Talent

Overview of Popular Industry-Specific Certifications in Data Science

Google, Amazon, and Facebook aren’t the only ones investing in ML anymore. If you know three companies, one of them is looking for machine learning talent.

Reskilling existing employees is the current most popular strategy for sourcing AI and machine learning talent.2 On average, organizations spend 200 hours per individual per year on apprenticeships, which include on-the-job training and offsite tech training programs—underscoring the industry-wide talent crunch.

The problem: The machine learning space is quickly evolving with increasing specialization in roles. A typical machine learning project requires a diverse team of engineers, researchers, and infrastructure specialists.

Most companies don’t know what they’re looking for (let alone, where to find it).

Business uses for machine learning are far-reaching and diverse

In business, machine learning helps us detect fraud and spot security risks. But it also talks to our customers, segments them for us, and personalizes their experiences.

In healthcare, it’s the basis of new treatments and therapies. Increasingly, patients (and their pockets) benefit from ML-enabled diagnostics, surgical planning, and precision medicine.

In transportation, it’s driving us home, enabling our same-day deliveries, and helping us avoid roadblocks (and speeding tickets).

The same technology? Yes.
Different technology? Also yes.

Types of Talent Within the ML Spectrum

When companies hire machine learning talent, they forget a critical part of the equation: The ML spectrum is broader than most people think. Depending on their goals and project scale, they need to source roles that specialize in specific tools and technologies.

Machine Learning Engineers are the rockstars who design, build, and deploy ML models. They’re well-versed in programming, and debugging complex algorithms is a breeze for them. They develop from scratch or re-engineer to produce powerful, accurate solutions.

ML Researchers have the technical and analytical skills to develop new algorithms and techniques. They live and breathe all things AI, from neural networks to decision trees. They create sophisticated models using NLP, deep learning, and reinforcement learning.

ML Infrastructure Engineers are the builders and trainers. They assemble the “backbone” for ML projects—namely, the frameworks and cloud infrastructure that enable the models to run. They’re experts in scripting languages, data storage systems, and distributed computing.

ML projects require varied skill sets

“Machine learning” comprises four distinct algorithms: supervised, semi-supervised, unsupervised, and reinforcement learning. Each algorithm is tailored to different objectives and calls for a unique combination of skills.

Supervised Learning

The “lead by example” approach. An operator gives a computer labeled training data and teaches it to recognize patterns by gradually pushing it toward the right answer. 

Examples: regression, classification, and forecasting.

Semi-Supervised Learning

The “mix and match” approach. Unlabeled data is used in conjunction with labeled data to teach a computer how to recognize patterns. 

Examples: web page categorization, image segmentation, and medical diagnosis.

Unsupervised Learning

The “teach it yourself” approach. With no labeled training data, unsupervised learning creates models that identify and separate clusters of information. 

Examples: clustering and dimension reduction.

Reinforcement Learning

The “trial and error” approach. The computer is rewarded or penalized based on its performance and is responsible for learning from mistakes, like a game of trial and error. 

Examples: robotics, natural language processing, and process automation.

Companies have different needs at different growth stages

What do startups and enterprise companies have in common? Both need machine learning talent.

What are their differences? Everything else.

Startups: The nimble and the scrappy

If a young tech company needs an ML specialist (they often don’t), they need one as agile as them. Simple yet varied functions require an engineer who can pivot between multiple departments—and communicate flawlessly.

The perfect ML hire for startups: A generalist

Mid-Market: The ambitious and the serious

Budding companies have more structure, more money, and less flexibility. Their projects are more complex and the results more impactful. A successful ML hire needs to be able to work independently to build the future of the company.

The perfect ML hire for mid-market: An innovator

Enterprises: The established and the risk-averse

Large companies have two distinct advantages in machine learning. They can afford experienced specialists (and the hardware they need), and they have the time to build elaborate solutions. Each specialist has less impact, but the collective effort is larger.

The perfect ML hire for enterprises: An architect

HopHR’s Tips for Distinguishing ML Talent, Skills, and Technologies

Five tips to make sure you hire the right person for the job:

  1. Assess company needs before hiring. Account for your company size, project complexity, and which technology is the best fit.
  2. Skills are nothing if your candidate can’t communicate. Candidates should be able to write clean, efficient code and explain concepts back to you in clear language.
  3. Look for framework finesse. At a minimum, experience with TensorFlow, PyTorch, and scikit-learn is a must.
  4. Seek out the industry-savvy. Domain-specific expertise is a must, especially if your industry is highly regulated.
  5. Soft skills are hard to teach. The best ML candidates have a creative eye, a knack for problem-solving, and communication skills that get your CEO on board.

Hiring headaches? We’ll take it from here…

Hiring needs vary by industry, company scale, and project scope. And picking the wrong candidate lands you back at square one.

You get back to working on what makes your product great. We’ll find the people to help you do so.

Blog FAQs

Reskilling existing employees has emerged as the most popular strategy for sourcing AI and machine learning talent due to the industry-wide talent crunch. Organizations are investing an average of 200 hours per individual annually in apprenticeships, which encompass on-the-job training and offsite tech training programs. This approach not only leverages the existing workforce but also aligns with the rapidly evolving nature and increasing specialization within the machine learning space.

Machine learning applications are diverse and have far-reaching implications across various sectors. In business, it aids in detecting fraud, enhancing security, and personalizing customer experiences. Healthcare sees benefits in the development of new treatments, diagnostics, and precision medicine, improving patient outcomes and cost-efficiency. In transportation, machine learning enables autonomous driving, efficient delivery services, and traffic management.

Machine learning encompasses four primary algorithms: supervised, semi-supervised, unsupervised, and reinforcement learning, each suited for different objectives.

  • Supervised learning involves training models with labeled data to recognize patterns, applicable in regression and classification tasks.
  • Semi-supervised learning combines labeled and unlabeled data for pattern recognition, useful in web page categorization and medical diagnosis.
  • Unsupervised learning, which operates without labeled training data, is ideal for clustering and dimension reduction.
  • Reinforcement learning, based on a system of rewards and penalties, finds applications in robotics and natural language processing.