Within the Creator Highlight sequence, TDS Editors chat with members of our group about their profession path in information 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 information 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 lately gained $10,000 in a machine studying competitors — congratulations! What was the largest lesson you took away from that have, and the way has it formed your strategy to real-world ML issues?
My greatest 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 normal credit score threat drawback. This attitude proved extra beneficial than any diploma or deep studying specialization.
This expertise basically 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 reasonably than over-engineering it. This is applicable on to trade 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 individual”, 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 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’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 beneficial methods that outlive your tenure, however you possibly can’t take them with you or get ongoing credit score for his or her success. Successful this competitors confirmed me I had the abilities to create my very own options reasonably than simply contributing to another person’s imaginative and prescient. I realized beneficial abilities in company roles, however I’m excited to use them to challenges I care deeply about.
I’m pursuing this by means of two most important paths: consulting tasks that leverage my information science and machine studying experience, and constructing an AI language studying product. The consulting work supplies 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 by means of my publication.
As a polyglot who speaks 9 languages, I’ve thought deeply concerning the challenges of reaching conversational fluency and never simply textbook information when studying a international language. I’m creating an AI language studying companion that helps folks follow real-world situations and cultural contexts.
What excites me most is the technical problem of constructing AI options that bear in mind cultural context and conversational nuance. On the consulting facet, I’m energized by working with firms that need to resolve actual issues reasonably than simply implementing AI for the sake of getting AI. Whether or not it’s engaged on threat fashions or streamlining data retrieval, I really like tasks the place area experience and sensible AI intersect.
Many firms are desperate to “do one thing with AI” however don’t at all times know the place to start out. What’s your typical course of for serving to a brand new consumer scope and prioritize their first AI initiative?
I take a problem-first strategy reasonably than lead with AI options. Too many firms need to “do one thing with AI” with out figuring out what particular enterprise drawback they’re making an attempt to unravel, which often results in spectacular demos that don’t transfer the needle.
My typical course of follows three steps:
First, I concentrate on drawback prognosis. We determine particular ache factors with measurable influence. For instance, I just lately labored with a consumer within the restaurant area going through slowing income development. As an alternative of leaping to an “AI-powered resolution,” we examined buyer assessment information to determine patterns. For instance, which menu objects drove complaints, what service parts generated constructive suggestions, and which operational points appeared most ceaselessly. This data-driven prognosis led to particular suggestions reasonably 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 are able to’t measure it, we are able to’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 perfect one. Typically that’s a visualization dashboard, typically it’s a RAG system, typically it’s including predictive capabilities. AI isn’t at all times the reply, however when it’s, we all know precisely why we’re utilizing it and what success seems like.
This strategy has delivered constructive outcomes. Shoppers sometimes see improved decision-making velocity and clearer information insights. Whereas I’m constructing my unbiased follow, specializing in actual issues reasonably than AI buzzwords has been key to consumer satisfaction and repeat engagements.
You’ve mentored aspiring information scientists — what’s one frequent pitfall you see amongst folks making an attempt to interrupt into the sector, and the way do you advise them to keep away from it?
The largest pitfall I see is making an attempt to study all the things 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 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 should be an professional at all the things.
My recommendation: Choose your lane first. Determine which function 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 over actual tasks (you possibly can learn my transition story right here). I leveraged my area experience in credit score and fraud threat, and utilized this to characteristic engineering and enterprise influence calculations.
The hot button is making use of these abilities to actual issues, not getting caught in tutorial hell. I see this sample continually by means of my publication and mentoring. Individuals who break by means of 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 solely?
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 beneficial, transferable abilities.
Right here’s how I’d strategy the choice:
In the event you’re coming from a enterprise background: Product information scientist roles are sometimes the simplest entry level. Concentrate on SQL, A/B testing, and information visualization abilities. These roles typically 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’ll construct on current software program growth abilities.
In the event you’re drawn to infrastructure: MLOps engineering is extremely in demand, particularly as extra firms deploy ML and AI fashions at scale.
The panorama retains evolving, however as talked about above, area experience typically issues greater than following the newest development. 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.
Concentrate on fixing actual issues in domains you perceive, then let the technical abilities comply with. To study extra about completely different roles, I’ve written concerning the 5 kinds of information science profession paths right here.
What’s one AI or information science matter you assume extra folks must be writing about or one development you’re watching intently proper now?
I’ve been blown away by the velocity and high quality of text-to-speech (TTS) know-how in mimicking actual conversational patterns and tone. I believe extra folks must be writing about TTS know-how for endangered language preservation.
As a polyglot who’s obsessed with cross-cultural understanding, I’m fascinated by how AI may assist stop languages from disappearing solely. 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 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 excited about the world, particular information methods, and cultural reminiscence that may’t be translated.
The development I’m watching intently is how switch studying and voice cloning are making this technically possible. We’re reaching a degree the place you would possibly solely want hours reasonably than 1000’s of hours of audio information to create high quality TTS for brand new languages, particularly utilizing current multilingual fashions. Whereas this know-how raises legitimate issues about misuse, purposes like language preservation present how we are able to 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 essentially the most attention-grabbing AI purposes typically 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 possibly can comply with her on TDS, Substack, or Linkedin.
