Spotlight on Emerging Skills – Share & earn a Community badge! 🔍

identify authors of recent AI/ML papers in Google Scholar, Semantic Scholar, ArXiv

Identify model and dataset contributors on Huggingface

Great idea ​@JayT! I tend to follow a lot of industry newsletters that help in surfacing thought leaders in different areas. Similar concept right? If you want to find the best, hang out where the best are hanging out. Thanks for sharing!

Love this topic! Emerging skills are changing so quickly that having good search strategies is becoming essential. One approach I’ve found helpful is combining skill keywords with industry-specific terms to surface profiles that might not explicitly list the newest buzzwords but still have the right experience.

I also like using Boolean search with synonyms for emerging skills — it’s surprising how many great candidates you can uncover that way. Curious to hear what other “hacks” people are using to stay ahead of the curve and find talent before those skills become mainstream!

​@Courtney-Community Manager Yes! I’ve been diving into AI to help analyze candidates I’ve passively sourced on LinkedIn—and it’s been a game changer. I’m finding that the more I experiment, the sharper my AI skills become. It really is about practicing consistently and being open to trial and error.

Right now, I’m building a list focused on Gen AI and RAD (Rapid Application Development) skills for a project, and I’ve noticed that the more I fine-tune my filters, the better the results. Sometimes I even uncover unexpected talent that I wouldn’t have found otherwise. You don’t know unless you try!

When I supported hiring for ARPA-H, I had to rethink my entire sourcing strategy.

Many of the roles were so new, they didn’t have standard titles yet and the skills we needed were often emerging or applied in unconventional ways.

ARPA-H focused on high-impact innovation across science, tech, and policy. That meant finding candidates who could bridge disciplines and work in fast-moving, experimental environments. These weren’t traditional AI roles, but they required expertise in areas like LLM fine-tuning, prompt engineering, and retrieval-augmented generation.

Because these skills weren’t always listed on resumes, I built out skill clusters and used them as sourcing anchors; looking for indicators like GitHub contributions, Kaggle projects, or published research to validate hands-on experience. Tools like LangChain, Hugging Face, and vector databases were key signals.

To surface the right talent, I used Boolean strings like:

("LLM" OR "prompt engineering") AND ("fine-tuning" OR "embedding") AND NOT ("intern" OR "student")

This helped me filter out early-career profiles and focus on candidates with practical experience.

LinkedIn Recruiter was my starting point, but I layered in GitHub, Arxiv, and Kaggle to get a fuller picture. For ARPA-H, this was essential as we weren’t only hiring based on what someone had done, but on what they could do in a rapidly evolving space.

I’ve been noticing a steady rise in demand for professionals who can bridge the gap between technical expertise and business problem-solving. Skills like Python, SQL, cloud platforms (Azure/GCP), and data engineering fundamentals are becoming baseline expectations across multiple industries.

At the same time, newer areas such as AI integration, prompt engineering, and automation-driven analytics are gaining traction, especially for roles that require adaptability and cross-functional collaboration.

From a sourcing perspective, combining skills-based Boolean searches with contextual keywords (like “workflow automation,” “data validation,” or “GenAI tools”) has helped uncover candidates who may not label themselves as AI experts yet clearly demonstrate emerging capabilities.

It’s interesting to see how these evolving skills are reshaping traditional role definitions and creating new career paths altogether.

Love all these - thanks for sharing ​@Ch Prathmesh Geetraj, ​@Najat Andreozzi, ​@Wendy Manninen, ​@Ehenjames & ​@JayT! Your badges have been awarded!

Yay!!!!

That’s a great boolean, I loved how you layerd it with GitHub, Arxiv and Kaggle, very outside the box!

Im allowing AI to do all of the following. It’s definitely a game changer. 

• ✔ AI sourcing script
• ✔ AI outreach personalization scripts
• ✔ AI job post templates
• ✔ AI “ideal candidate persona”
• ✔ AI interview guide
• ✔ AI onboarding system
• ✔ 10-message follow-up automation

This is really great ​@Tracie_Bouye I really like how you are maximizing AI to do the transactional things that take more time to complete. I feel that AI has definitely changed how we used to work! I would learn what prompts you use!

Thanks Wendy! Here’s an example:

Generate a sourcing toolkit for the following role. 

Role: [Role]

Industry: [Industry]

Seniority/Level: [Seniority]

Tone: [Tone]

Company: [Company

1. **Sourcing Script**

- Include search strategies, Boolean strings, keywords, and channels (LinkedIn, Indeed, GitHub, job boards, forums).

- Focus on passive candidates.

- Prioritize candidates by experience, skills, and [Seniority] level

Thanks for the revive ​@Tracie_Bouye and great to ‘see’ you! Love the collab between you and Wendy ​@Wendy Manninen 👏🏼

New Skills Scout badge has been awarded!

Hi Courtney! It’s great to hear from you! I love this community. There are so many sharp people to get fresh ideas from. I may not comment on everyone’s post but trust me, I take notes. 
 

​@Wendy Manninen  if the example helps and you need more prompts, let me know. I love telling AI what to do. 😂 

Thanks for the badge! 
 

You can include Persona too - You are a TA expert with 10+ years of experience and have worked in a cross-section of industries, teams, multiple geographies, high volume and niche role teams..

You can also embed a responsible and ethical framework as follows: 

• Utilize only publicly available information (LinkedIn, GitHub, StackOverflow, Kaggle etc.) that are open to search engines)

• Avoid scraping of hidden data or data obtained by bypassing terms of service of website

• Avoid excluding candidates based on gender, religion, age, ethnicity, caste, etc.

• Use only skill and role based anchors, and avoid demographic filters.

• Consider inclusion of under-represented groups (e.g., veterans, people with disabilities, LGBTQ+)

• Don't focus only on candidates with Premium institutes background like IIT, NIT, Ivy Leagues and also consider alternate career paths (boot camps, open-source contributors, not just formal degrees).

Thanks for sharing this ​@JayT ! Love the feedback. 
 

Yes it does thank you ​@Tracie_Bouye