How can AI professionals ace their phone interviews?

I've received feedback from people believing they have to be pure play software developers in order to get an "AI-based role". This could not be further from the truth. One of the biggest pieces of value, especially from folks within the Operations space is to understand how & where artificial intelligence can be used within their industry/area of expertise. Most of these folks already know the manual, inefficient areas that should be automated using artificial intelligence. Diving deeper within the top AI tools and what is commonly used will bridge the gap and make you a much more valuable candidate for a role involving AI as technology companies consistently seek a mix of industry-expertise and foundational technology knowledge.

A distinctive approach is to analyze a startup’s seed round pitch deck, if accessible. This deck is a blueprint of the company’s vision, strategy, and market approach share vital insights. By understanding the core hypotheses and the initial roadmap, you can reveal the evolution of their business model. Discussing this evolution during an interview demonstrates a sophisticated level of due diligence and strategic thought, showing that you are invested in the company’s journey and pote

Don’t take your background for granted, a mistake I often see with engineers. While you focus on the content of the interview, your environment also speaks volumes. Ensure your background is professional and clutter-free; any item within view should be intentional. Good lighting and a properly angled camera are not just for influencers but are crucial for creating a positive visual impression. These pragmatic details can subtly influence the interviewer's perception and demonstrate your attention to detail.

Have a notepad/paper handy to note any points Your background matters a lot, it should be peaceful Your internet, charging are most important too Do not engage any other calls or door bells or kids

For AI professionals, honing their skills is vital. First, focus on coding proficiency, especially in Python and AI libraries like TensorFlow or PyTorch. Practice solving AI-related coding challenges and algorithms. Second, engage in machine learning projects. Independently tackle AI initiatives or contribute to open-source projects. Document these projects thoroughly, including descriptions, datasets used, applied algorithms, and achieved results. Third, stay updated with the ever-evolving AI field. Dive into research papers, follow AI blogs, and actively participate in online AI communities to keep abreast of the latest techniques and breakthroughs. This practical experience enhances your problem-solving abilities and demonstrates .

Executing AI/ML projects is an art of balancing algorithms & data with intuition & creativity. Succeeding in interviews involves showcasing both tech expertise & the artistry inherent in the field. Here are a few i'view strategies: Portfolio of Projects: Showcase a range of projects - academic, personal, or contributions to open-source. Highlight your tech skills & creative approach to problem-solving. Storytelling: Try explaining complex concepts or projects via a story. Ability to communicate complex ideas effectively is a crucial skill in AI/ML field. Reflective Learning: Discuss what you learned from projects that didn’t go as planned. This shows your ability to learn from failure, a key component of the iterative process in AI/ML.

To convey personality without being seen as a risky hire, it’s about striking a balance. Share personal insights that reflect your passion for your work and your collaborative spirit, not personal views that could be contentious. For instance, talking about a team project that excited you or a volunteer experience that improved your leadership skills is professional and humanizing. It's important because it shows you can be both personable and a cultural fit, without triggering potential HR concerns by oversharing or touching on sensitive topics.

Personality is a huge differentiator for AI professionals. Most applicants are assumed to have solid technical skillsets. But the sift skills for tech positions are always a wildcard. Those with solid communication skills and personal demeanors stand out.

Some of the most powerful tools of experienced interviewees are: 4. The interview thank you letter 3. The pre-interview confirmation message 2. The mid-process LinkedIn connection invites 1. The response to a turndown Dwelling on just that #1 item... You're going to get turned down. It's going to happen. Remember, you are Kenough. Seriously, though, rejection hurts and you're either going to experience it or you're experiencing it now. Here's the thing; about 20% of offers fall through. So if you're the "runner up", undesirable as that status may be, when you respond with grace, positivity, and curiosity about future opportunities you're the one standing there ready when the door is left open by someone declining the offer.

Feedback should be acceptable and acknowledgement should be respected. Kindly ensure that these are taken care of and that adds value to the oneself

The candidate has to speak about the AI projects the candidated lead or participated in. Showing real industry/reseaech experience will attract the recruiter.

The role will always be more than pure data science. You need to appreciate that you will be delivering to a team that will not understand a word that you are saying. Honestly, they do not care. They only care about achieving a result and doing so in the quickest possible time. You need to roll up your sleaves and understand what is important to the business, and describe it in their words. Demonstrate your business acumen and data science skills together, and you will stand out from the crowd