Within the Writer Highlight collection, TDS Editors chat with members of our group about their profession path in knowledge science and AI, their writing, and their sources of inspiration. In the present day, 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 an ordinary credit score danger drawback. This attitude proved extra worthwhile than any diploma or deep studying specialization.
This expertise basically 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 quite than over-engineering it. This is applicable on to trade 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 limitations 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 looking back, 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 give attention to 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’m going.
Your profession path spans enterprise, public coverage, machine studying, and now AI Marketing consultant. 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 shoppers are you most excited to work with?
The shift to unbiased work was pushed by wanting to construct one thing I may really personal and develop. In company roles, you construct worthwhile programs that outlive your tenure, however you’ll be able to’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 discovered worthwhile expertise in company roles, however I’m excited to use them to challenges I care deeply about.
I’m pursuing this by two fundamental paths: consulting tasks that leverage my knowledge science and machine studying experience, and constructing an AI language studying product. The consulting work offers fast 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 by my 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 creating an AI language studying accomplice that helps folks apply real-world situations and cultural contexts.
What excites me most is the technical problem of constructing AI options that take note of cultural context and conversational nuance. On the consulting aspect, I’m energized by working with corporations that wish to resolve actual issues quite than simply implementing AI for the sake of getting AI. Whether or not it’s engaged on danger fashions or streamlining data retrieval, I really like tasks the place area experience and sensible AI intersect.
Many corporations are wanting to “do one thing with AI” however don’t all the time know the place to begin. 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 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 give attention to drawback analysis. We establish particular ache factors with measurable impression. For instance, I lately labored with a shopper within the restaurant area dealing with slowing income development. As an alternative of leaping to an “AI-powered answer,” we examined buyer assessment knowledge to establish patterns. For instance, which menu objects drove complaints, what service parts generated optimistic suggestions, and which operational points appeared most often. This data-driven analysis 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 know-how.
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 seems like.
This method has delivered optimistic outcomes. Purchasers usually see improved decision-making pace and clearer knowledge insights. Whereas I’m constructing my unbiased apply, specializing in actual issues quite 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 folks attempting to interrupt into the sector, and the way do you advise them to keep away from it?
The largest pitfall I see is attempting to study all the pieces as a substitute 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 skilled at all the pieces.
My recommendation: Choose your lane first. Determine which function excites you most, then give attention to sharpening these core expertise. I personally transitioned from analyst to ML engineer by intensely learning machine studying and taking up actual tasks (you’ll be able to learn my transition story here). I leveraged my area experience in credit score and fraud danger, and utilized this to function engineering and enterprise impression calculations.
The secret is making use of these expertise to actual issues, not getting caught in tutorial hell. I see this sample continually by my e-newsletter and mentoring. Individuals who break by 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 totally?
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 worthwhile, transferable expertise.
Right here’s how I’d method the choice:
For those who’re coming from a enterprise background: Product knowledge scientist roles are sometimes the simplest entry level. Give attention to SQL, A/B testing, and knowledge visualization expertise. These roles usually worth enterprise instinct over deep technical expertise.
In case you have programming expertise: Contemplate ML engineering or AI engineering. The demand is excessive, and you may construct on current software program growth expertise.
For those who’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 newest development. I gained 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 expertise observe. To study extra about totally different roles, I’ve written concerning the 5 sorts of knowledge science profession paths here.
What’s one AI or knowledge science matter you suppose extra folks needs to be writing about or one development you’re watching carefully proper now?
I’ve been blown away by the pace and high quality of text-to-speech (TTS) know-how in mimicking actual conversational patterns and tone. I feel extra folks needs to be writing about TTS know-how for endangered language preservation.
As a polyglot who’s keen about cross-cultural understanding, I’m fascinated by how AI may assist forestall languages from disappearing totally. Most TTS growth focuses on main languages with huge datasets, however there are over 7,000 languages worldwide, and plenty of are susceptible to extinction.
What excites me is the potential for AI to create voice synthesis for languages that may solely have a couple of hundred audio system left. That is know-how serving humanity and cultural preservation at its greatest! When a language dies, we lose distinctive methods of fascinated by the world, particular data programs, and cultural reminiscence that may’t be translated.
The development I’m watching carefully is how switch studying and voice cloning are making this technically possible. We’re reaching some extent the place you would possibly solely want hours quite than hundreds of hours of audio knowledge to create high quality TTS for brand spanking new languages, particularly utilizing current multilingual fashions. Whereas this know-how raises legitimate considerations about misuse, purposes 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 apply, I’m continually reminded that essentially the most attention-grabbing AI purposes 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 along with her newest articles, you’ll be able to observe her on TDS, Substack, or Linkedin.
