Within the Creator Highlight sequence, TDS Editors chat with members of our group about their profession path in knowledge science and AI, their writing, and their sources of inspiration. At the moment, we’re thrilled to share our dialog with Claudia Ng.
Claudia is an AI entrepreneur and knowledge scientist with 6+ years of expertise constructing manufacturing machine studying fashions in FinTech. She positioned second and gained $10,000 in a Web3 credit score scoring ML competitors in 2024.
You recently won $10,000 in a machine learning competition — congratulations! What was the most important lesson you took away from that have, and the way has it formed your method to real-world ML issues?
My largest lesson was realizing that area experience issues greater than algorithmic complexity. It was a Web3 credit score scoring ML competitors, and regardless of by no means having labored with blockchain knowledge or neural networks for credit score scoring, my 6+ years in FinTech gave me the enterprise instinct to deal with this as a regular credit score threat drawback. This angle proved extra precious than any diploma or deep studying specialization.
This expertise essentially shifted how I method ML issues in two methods:
First, I discovered that shipped is healthier than good. I spent solely 10 hours on the competitors and submitted an “MVP” method slightly than over-engineering it. This is applicable on to business work: an honest mannequin working in manufacturing delivers extra worth than a extremely optimized mannequin sitting in a Jupyter pocket book.
Second, I found that the majority obstacles are psychological, not technical. I virtually didn’t enter as a result of I didn’t know Web3 or really feel like a “competitors particular person”, however on reflection, I used to be overthinking it. Whereas I’m nonetheless engaged on making use of this lesson extra broadly, it has modified how I consider alternatives. I now deal with whether or not I perceive the core drawback and whether or not it excites me, and belief that I’ll be capable to determine it out as I am going.
Your profession path spans enterprise, public coverage, machine studying, and now AI Guide. What motivated your shift from company tech to the AI freelance world, and what excites you most about this new chapter? What sorts of challenges or purchasers are you most excited to work with?
The shift to unbiased work was pushed by wanting to construct one thing I may actually personal and develop. In company roles, you construct precious methods that outlive your tenure, however you possibly can’t take them with you or get ongoing credit score for his or her success. Profitable this competitors confirmed me I had the abilities to create my very own options slightly than simply contributing to another person’s imaginative and prescient. I discovered precious abilities in company roles, however I’m excited to use them to challenges I care deeply about.
I’m pursuing this via two most important paths: consulting initiatives that leverage my knowledge science and machine studying experience, and constructing an AI language studying product. The consulting work gives quick income and retains me related to actual enterprise issues, whereas the language product represents my long-term imaginative and prescient. I’m studying to construct in public and sharing my journey via my newsletter.
As a polyglot who speaks 9 languages, I’ve thought deeply in regards to the challenges of attaining conversational fluency and never simply textbook data when studying a overseas language. I’m creating an AI language studying companion that helps individuals follow real-world eventualities and cultural contexts.
What excites me most is the technical problem of constructing AI options that keep in mind cultural context and conversational nuance. On the consulting facet, I’m energized by working with corporations that wish to clear up actual issues slightly than simply implementing AI for the sake of getting AI. Whether or not it’s engaged on threat fashions or streamlining info retrieval, I like initiatives the place area experience and sensible AI intersect.
Many corporations are desperate to “do one thing with AI” however don’t all the time know the place to start out. What’s your typical course of for serving to a brand new shopper scope and prioritize their first AI initiative?
I take a problem-first method slightly than lead with AI options. Too many corporations wish to “do one thing with AI” with out figuring out what particular enterprise drawback they’re making an attempt to unravel, which normally results in spectacular demos that don’t transfer the needle.
My typical course of follows three steps:
First, I deal with drawback prognosis. We determine particular ache factors with measurable influence. For instance, I not too long ago labored with a shopper within the restaurant area dealing with slowing income progress. As an alternative of leaping to an “AI-powered resolution,” we examined buyer overview knowledge to determine patterns. For instance, which menu gadgets drove complaints, what service components generated constructive suggestions, and which operational points appeared most often. This data-driven prognosis led to particular suggestions slightly than generic AI implementations.
Second, we outline success upfront. I insist on quantifiable metrics like time financial savings, high quality enhancements, or income will increase. If we will’t measure it, we will’t show it labored. This prevents scope creep and ensures we’re fixing actual issues, not simply constructing cool expertise.
Third, we undergo viable options and align on the most effective one. Typically that’s a visualization dashboard, typically it’s a RAG system, typically it’s including predictive capabilities. AI isn’t all the time the reply, however when it’s, we all know precisely why we’re utilizing it and what success appears to be like like.
This method has delivered constructive outcomes. Purchasers usually see improved decision-making pace and clearer knowledge insights. Whereas I’m constructing my unbiased follow, specializing in actual issues slightly than AI buzzwords has been key to shopper satisfaction and repeat engagements.
You’ve mentored aspiring knowledge scientists — what’s one frequent pitfall you see amongst individuals making an attempt to interrupt into the sphere, and the way do you advise them to keep away from it?
The largest pitfall I see is making an attempt to be taught all the pieces as an alternative of specializing in one function. Many individuals, together with myself early on, really feel like they should take each AI course and grasp each idea earlier than they’re “certified.”
The truth is that knowledge science encompasses very totally different roles: from product knowledge scientists working A/B checks to ML engineers deploying fashions in manufacturing. You don’t have to be an knowledgeable at all the pieces.
My recommendation: Decide your lane first. Determine which function excites you most, then deal with sharpening these core abilities. I personally transitioned from analyst to ML engineer by intensely finding out machine studying and taking over actual initiatives (you possibly can learn my transition story here). I leveraged my area experience in credit score and fraud threat, and utilized this to characteristic engineering and enterprise influence calculations.
The bottom line is making use of these abilities to actual issues, not getting caught in tutorial hell. I see this sample continually via my publication and mentoring. Individuals who break via are those who begin constructing, even once they don’t really feel prepared.
The panorama of AI roles retains evolving. How ought to newcomers resolve the place to focus — ML engineering, knowledge analytics, LLMs, or one thing else fully?
Begin along with your present ability set and what pursuits you, not what sounds most prestigious. I’ve labored throughout totally different roles (analyst, knowledge scientist, ML engineer) and every introduced precious, transferable abilities.
Right here’s how I’d method the choice:
Should you’re coming from a enterprise background: Product knowledge scientist roles are sometimes the simplest entry level. Deal with SQL, A/B testing, and knowledge visualization abilities. These roles usually worth enterprise instinct over deep technical abilities.
When you’ve got programming expertise: Take into account ML engineering or AI engineering. The demand is excessive, and you may construct on present software program growth abilities.
Should you’re drawn to infrastructure: MLOps engineering is very in demand, particularly as extra corporations deploy ML and AI fashions at scale.
The panorama retains evolving, however as talked about above, area experience usually issues greater than following the newest pattern. I gained that ML competitors as a result of I understood credit score threat fundamentals, not as a result of I knew the fanciest algorithms.
Deal with fixing actual issues in domains you perceive, then let the technical abilities comply with. To be taught extra about totally different roles, I’ve written in regards to the 5 forms of knowledge science profession paths here.
What’s one AI or knowledge science matter you assume extra individuals must be writing about or one pattern you’re watching carefully proper now?
I’ve been blown away by the pace and high quality of text-to-speech (TTS) expertise in mimicking actual conversational patterns and tone. I feel extra individuals must be writing about TTS expertise for endangered language preservation.
As a polyglot who’s enthusiastic about cross-cultural understanding, I’m fascinated by how AI may assist forestall languages from disappearing fully. Most TTS growth focuses on main languages with huge datasets, however there are over 7,000 languages worldwide, and plenty of are prone to extinction.
What excites me is the potential for AI to create voice synthesis for languages that may solely have a number of hundred audio system left. That is expertise serving humanity and cultural preservation at its finest! When a language dies, we lose distinctive methods of interested by the world, particular data methods, and cultural reminiscence that may’t be translated.
The pattern I’m watching carefully is how switch studying and voice cloning are making this technically possible. We’re reaching some extent the place you may solely want hours slightly than hundreds of hours of audio knowledge to create high quality TTS for brand new languages, particularly utilizing present multilingual fashions. Whereas this expertise raises legitimate issues about misuse, functions like language preservation present how we will use these capabilities responsibly for cultural good.
As I proceed creating my language studying product and constructing my consulting follow, I’m continually reminded that probably the most fascinating AI functions usually come from combining technical capabilities with deep area understanding. Whether or not it’s constructing machine studying fashions or cultural communication instruments, the magic occurs on the intersection.
To be taught extra about Claudia‘s work and keep up-to-date together with her newest articles, you possibly can comply with her on TDS, Substack, or Linkedin.
