Within the Writer Highlight sequence, TDS Editors chat with members of our neighborhood about their profession path in knowledge science and AI, their writing, and their sources of inspiration. As we speak, 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 lately gained $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 method 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 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 downside. This angle proved extra priceless than any diploma or deep studying specialization.
This expertise basically shifted how I method 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” method somewhat than over-engineering it. This is applicable on to trade work: an honest mannequin operating in manufacturing delivers extra worth than a extremely optimized mannequin sitting in a Jupyter pocket book.
Second, I found that almost all obstacles are psychological, not technical. I nearly 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 downside 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 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 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 priceless 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 abilities to create my very own options somewhat than simply contributing to another person’s imaginative and prescient. I realized priceless abilities in company roles, however I’m excited to use them to challenges I care deeply about.
I’m pursuing this by way of two important paths: consulting initiatives that leverage my knowledge science and machine studying experience, and constructing an AI language studying product. The consulting work offers 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 way of my e-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 international language. I’m creating an AI language studying accomplice that helps folks follow 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 remedy actual issues somewhat than simply implementing AI for the sake of getting AI. Whether or not it’s engaged on threat fashions or streamlining data retrieval, I like initiatives the place area experience and sensible AI intersect.
Many corporations are desirous to “do one thing with AI” however don’t at all times know the place to begin. 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 method somewhat than lead with AI options. Too many corporations wish to “do one thing with AI” with out figuring out what particular enterprise downside they’re attempting to resolve, which normally results in spectacular demos that don’t transfer the needle.
My typical course of follows three steps:
First, I give attention to downside 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 evaluation knowledge to determine patterns. For instance, which menu objects drove complaints, what service parts generated optimistic suggestions, and which operational points appeared most ceaselessly. This data-driven prognosis led to particular suggestions somewhat 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 expertise.
Third, we undergo viable options and align on the very best one. Generally 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 appears like.
This method has delivered optimistic outcomes. Purchasers sometimes see improved decision-making pace and clearer knowledge insights. Whereas I’m constructing my unbiased follow, specializing in actual issues somewhat than AI buzzwords has been key to consumer satisfaction and repeat engagements.
You’ve mentored aspiring knowledge scientists — what’s one frequent pitfall you see amongst folks attempting to interrupt into the sphere, and the way do you advise them to keep away from it?
The largest pitfall I see is attempting to be taught the whole lot 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 fact is that knowledge science encompasses very totally different roles: from product knowledge scientists operating A/B assessments to ML engineers deploying fashions in manufacturing. You don’t should be an skilled at the whole lot.
My recommendation: Choose your lane first. Determine which function excites you most, then give attention to sharpening these core abilities. I personally transitioned from analyst to ML engineer by intensely learning machine studying and taking up actual initiatives (you’ll be able to 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 secret’s making use of these abilities to actual issues, not getting caught in tutorial hell. I see this sample continually by way of my e-newsletter and mentoring. Individuals who break by way 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, knowledge analytics, LLMs, or one thing else totally?
Begin along with your present talent 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 priceless, transferable abilities.
Right here’s how I’d method the choice:
In case 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.
You probably have programming expertise: Think about ML engineering or AI engineering. The demand is excessive, and you’ll construct on present software program growth abilities.
In case you’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 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 right here.
What’s one AI or knowledge science matter you suppose extra folks must be writing about or one pattern you’re watching intently 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 folks must be writing about TTS expertise for endangered language preservation.
As a polyglot who’s captivated with 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 lots of are prone 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 finest! When a language dies, we lose distinctive methods of fascinated about the world, particular data programs, 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 a degree the place you may solely want hours somewhat than 1000’s of hours of audio knowledge to create high quality TTS for brand new languages, particularly utilizing present multilingual fashions. Whereas this expertise raises legitimate considerations about misuse, functions 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 probably the most attention-grabbing 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’ll be able to comply with her on TDS, Substack, or Linkedin.
