Within the Creator Highlight collection, TDS Editors chat with members of our group about their profession path in information science and AI, their writing, and their sources of inspiration. At present, we’re thrilled to share our dialog with Claudia Ng.
Claudia is an AI entrepreneur and information scientist with 6+ years of expertise constructing manufacturing machine studying fashions in FinTech. She positioned second and received $10,000 in a Web3 credit score scoring ML competitors in 2024.
You latterly received $10,000 in a machine studying competitors — congratulations! What was the most important lesson you took away from that have, and the way has it formed your strategy 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 information 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 danger drawback. This attitude proved extra invaluable than any diploma or deep studying specialization.
This expertise essentially shifted how I strategy ML issues in two methods:
First, I realized that shipped is best than good. I spent solely 10 hours on the competitors and submitted an “MVP” strategy quite than over-engineering it. This is applicable on to business work: a good mannequin operating in manufacturing delivers extra worth than a extremely optimized mannequin sitting in a Jupyter pocket book.
Second, I found that the majority boundaries 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 concentrate on whether or not I perceive the core drawback and whether or not it excites me, and belief that I’ll be capable of determine it out as I am going.
Your profession path spans enterprise, public coverage, machine studying, and now AI Advisor. 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 impartial work was pushed by wanting to construct one thing I might actually personal and develop. In company roles, you construct invaluable methods that outlive your tenure, however you may’t take them with you or get ongoing credit score for his or her success. Successful this competitors confirmed me I had the talents to create my very own options quite than simply contributing to another person’s imaginative and prescient. I realized invaluable abilities in company roles, however I’m excited to use them to challenges I care deeply about.
I’m pursuing this by two major paths: consulting tasks that leverage my information science and machine studying experience, and constructing an AI language studying product. The consulting work gives quick income and retains me linked 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 by my e-newsletter.
As a polyglot who speaks 9 languages, I’ve thought deeply concerning the challenges of attaining conversational fluency and never simply textbook data when studying a international language. I’m growing an AI language studying companion that helps individuals observe real-world situations and cultural contexts.
What excites me most is the technical problem of constructing AI options that take into consideration cultural context and conversational nuance. On the consulting aspect, I’m energized by working with corporations that wish to clear up actual issues quite than simply implementing AI for the sake of getting AI. Whether or not it’s engaged on danger fashions or streamlining info retrieval, I like tasks 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 strategy quite 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 attempting to unravel, which normally results in spectacular demos that don’t transfer the needle.
My typical course of follows three steps:
First, I concentrate on drawback prognosis. We establish particular ache factors with measurable affect. For instance, I lately labored with a shopper within the restaurant area going through slowing income progress. As an alternative of leaping to an “AI-powered resolution,” we examined buyer overview information to establish patterns. For instance, which menu gadgets drove complaints, what service parts generated constructive suggestions, and which operational points appeared most often. This data-driven prognosis led to particular suggestions quite 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. Generally 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 like.
This strategy has delivered constructive outcomes. Shoppers sometimes see improved decision-making velocity and clearer information insights. Whereas I’m constructing my impartial observe, specializing in actual issues quite than AI buzzwords has been key to shopper satisfaction and repeat engagements.
You’ve mentored aspiring information scientists — what’s one frequent pitfall you see amongst individuals attempting to interrupt into the sector, and the way do you advise them to keep away from it?
The most important pitfall I see is attempting to study every little thing as an alternative of specializing in one position. 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 information science encompasses very completely different roles: from product information scientists operating A/B assessments to ML engineers deploying fashions in manufacturing. You don’t have to be an skilled at every little thing.
My recommendation: Choose your lane first. Work out which position excites you most, then concentrate on sharpening these core abilities. I personally transitioned from analyst to ML engineer by intensely learning machine studying and taking up actual tasks (you may learn my transition story right here). I leveraged my area experience in credit score and fraud danger, and utilized this to characteristic engineering and enterprise affect calculations.
The hot button is making use of these abilities to actual issues, not getting caught in tutorial hell. I see this sample continuously by my e-newsletter and mentoring. Individuals who break by are those who begin constructing, even after they don’t really feel prepared.
The panorama of AI roles retains evolving. How ought to newcomers determine the place to focus — ML engineering, information analytics, LLMs, or one thing else totally?
Begin together with your present talent set and what pursuits you, not what sounds most prestigious. I’ve labored throughout completely different roles (analyst, information scientist, ML engineer) and every introduced invaluable, transferable abilities.
Right here’s how I’d strategy the choice:
If you happen to’re coming from a enterprise background: Product information scientist roles are sometimes the simplest entry level. Give attention to SQL, A/B testing, and information visualization abilities. These roles usually worth enterprise instinct over deep technical abilities.
When you have programming expertise: Think about ML engineering or AI engineering. The demand is excessive, and you’ll construct on present software program growth abilities.
If you happen to’re drawn to infrastructure: MLOps engineering is extremely 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 most recent pattern. I received that ML competitors as a result of I understood credit score danger fundamentals, not as a result of I knew the fanciest algorithms.
Give attention to fixing actual issues in domains you perceive, then let the technical abilities observe. To study extra about completely different roles, I’ve written concerning the 5 varieties of information science profession paths right here.
What’s one AI or information science subject you suppose extra individuals must be writing about or one pattern you’re watching intently proper now?
I’ve been blown away by the velocity and high quality of text-to-speech (TTS) expertise in mimicking actual conversational patterns and tone. I believe extra individuals must be writing about TTS expertise for endangered language preservation.
As a polyglot who’s obsessed with cross-cultural understanding, I’m fascinated by how AI might assist stop languages from disappearing totally. Most TTS growth focuses on main languages with large datasets, however there are over 7,000 languages worldwide, and lots of are susceptible to extinction.
What excites me is the potential for AI to create voice synthesis for languages which may solely have a number of hundred audio system left. That is expertise serving humanity and cultural preservation at its greatest! When a language dies, we lose distinctive methods of eager about the world, particular data methods, and cultural reminiscence that may’t be translated.
The pattern I’m watching intently is how switch studying and voice cloning are making this technically possible. We’re reaching some extent the place you may solely want hours quite than 1000’s of hours of audio information to create high quality TTS for brand spanking 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 growing my language studying product and constructing my consulting observe, I’m continuously reminded that essentially 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 study extra about Claudia‘s work and keep up-to-date together with her newest articles, you may observe her on TDS, Substack, or Linkedin.
