Within the Writer 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. As we speak, 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 latterly 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 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 regular credit score threat downside. This attitude proved extra priceless than any diploma or deep studying specialization.
This expertise basically shifted how I strategy ML issues in two methods:
First, I discovered that shipped is best than excellent. I spent solely 10 hours on the competitors and submitted an “MVP” strategy relatively than over-engineering it. This is applicable on to business work: a good mannequin working in manufacturing delivers extra worth than a extremely optimized mannequin sitting in a Jupyter pocket book.
Second, I found that almost all boundaries 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 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 downside and whether or not it excites me, and belief that I’ll have the ability to 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 really personal and develop. In company roles, you construct priceless 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 talents to create my very own options relatively than simply contributing to another person’s imaginative and prescient. I discovered priceless expertise in company roles, however I’m excited to use them to challenges I care deeply about.
I’m pursuing this by way of two essential paths: consulting initiatives that leverage my information science and machine studying experience, and constructing an AI language studying product. The consulting work gives fast 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 information when studying a international language. I’m creating an AI language studying associate that helps individuals 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 aspect, I’m energized by working with corporations that wish to clear up actual issues relatively 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 wanting 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 shopper scope and prioritize their first AI initiative?
I take a problem-first strategy relatively 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 making an attempt to resolve, which normally results in spectacular demos that don’t transfer the needle.
My typical course of follows three steps:
First, I concentrate on downside analysis. We establish particular ache factors with measurable influence. For instance, I lately labored with a shopper within the restaurant area going through slowing income progress. As a substitute of leaping to an “AI-powered resolution,” we examined buyer assessment information to establish patterns. For instance, which menu objects drove complaints, what service parts generated constructive suggestions, and which operational points appeared most steadily. This data-driven analysis led to particular suggestions relatively 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. 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 seems like.
This strategy has delivered constructive outcomes. Purchasers sometimes see improved decision-making pace and clearer information insights. Whereas I’m constructing my impartial follow, specializing in actual issues relatively than AI buzzwords has been key to shopper satisfaction and repeat engagements.
You’ve mentored aspiring information scientists — what’s one widespread 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 study every little thing 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 information science encompasses very completely different roles: from product information scientists working A/B exams 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 function excites you most, then concentrate on sharpening these core expertise. I personally transitioned from analyst to ML engineer by intensely learning machine studying and taking up actual initiatives (you possibly can learn my transition story right here). I leveraged my area experience in credit score and fraud threat, and utilized this to function engineering and enterprise influence calculations.
The secret is making use of these expertise to actual issues, not getting caught in tutorial hell. I see this sample always by way of my e-newsletter and mentoring. Individuals who break by way of are those who begin constructing, even once 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 along with your present ability 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 priceless, transferable expertise.
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 best entry level. Give attention to SQL, A/B testing, and information visualization expertise. These roles typically worth enterprise instinct over deep technical expertise.
If in case you have programming expertise: Contemplate ML engineering or AI engineering. The demand is excessive, and you may construct on current software program improvement expertise.
In the event 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 typically 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.
Give attention to fixing actual issues in domains you perceive, then let the technical expertise observe. To study extra about completely different roles, I’ve written in regards to the 5 forms of information science profession paths right here.
What’s one AI or information science matter you assume extra individuals ought to 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) know-how in mimicking actual conversational patterns and tone. I believe extra individuals ought to be writing about TTS know-how for endangered language preservation.
As a polyglot who’s captivated with cross-cultural understanding, I’m fascinated by how AI might assist forestall languages from disappearing totally. Most TTS improvement focuses on main languages with huge datasets, however there are over 7,000 languages worldwide, and plenty of are vulnerable to extinction.
What excites me is the potential for AI to create voice synthesis for languages which may solely have just a few 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 enthusiastic about the world, particular information 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 relatively than 1000’s of hours of audio information to create high quality TTS for brand spanking new languages, particularly utilizing current multilingual fashions. Whereas this know-how raises legitimate issues 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 follow, I’m always 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 observe her on TDS, Substack, or Linkedin.
