In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science and AI, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Claudia Ng.
Claudia is an AI entrepreneur and data scientist with 6+ years of experience building production machine learning models in FinTech. She placed second and won $10,000 in a Web3 credit scoring ML competition in 2024.
You recently won $10,000 in a machine learning competition — congratulations! What was the biggest lesson you took away from that experience, and how has it shaped your approach to real-world ML problems?
My biggest lesson was realizing that domain expertise matters more than algorithmic complexity. It was a Web3 credit scoring ML competition, and despite never having worked with blockchain data or neural networks for credit scoring, my 6+ years in FinTech gave me the business intuition to treat this as a standard credit risk problem. This perspective proved more valuable than any degree or deep learning specialization.
This experience fundamentally shifted how I approach ML problems in two ways:
First, I learned that shipped is better than perfect. I spent only 10 hours on the competition and submitted an “MVP” approach rather than over-engineering it. This applies directly to industry work: a decent model running in production delivers more value than a highly optimized model sitting in a Jupyter notebook.
Second, I discovered that most barriers are mental, not technical. I almost didn’t enter because I didn’t know Web3 or feel like a “competition person”, but in retrospect, I was overthinking it. While I’m still working on applying this lesson more broadly, it has changed how I evaluate opportunities. I now focus on whether I understand the core problem and whether it excites me, and trust that I’ll be able to figure it out as I go.
Your career path spans business, public policy, machine learning, and now AI Consultant. What motivated your shift from corporate tech to the AI freelance world, and what excites you most about this new chapter? What kinds of challenges or clients are you most excited to work with?
The shift to independent work was driven by wanting to build something I could truly own and grow. In corporate roles, you build valuable systems that outlive your tenure, but you can’t take them with you or get ongoing credit for their success. Winning this competition showed me I had the skills to create my own solutions rather than just contributing to someone else’s vision. I learned valuable skills in corporate roles, but I’m excited to apply them to challenges I care deeply about.
I’m pursuing this through two main paths: consulting projects that leverage my data science and machine learning expertise, and building an AI language learning product. The consulting work provides immediate revenue and keeps me connected to real business problems, while the language product represents my long-term vision. I’m learning to build in public and sharing my journey through my newsletter.
As a polyglot who speaks nine languages, I’ve thought deeply about the challenges of achieving conversational fluency and not just textbook knowledge when learning a foreign language. I’m developing an AI language learning partner that helps people practice real-world scenarios and cultural contexts.
What excites me most is the technical challenge of building AI solutions that take into account cultural context and conversational nuance. On the consulting side, I’m energized by working with companies that want to solve real problems rather than just implementing AI for the sake of having AI. Whether it’s working on risk models or streamlining information retrieval, I love projects where domain expertise and practical AI intersect.
Many companies are eager to “do something with AI” but don’t always know where to start. What’s your typical process for helping a new client scope and prioritize their first AI initiative?
I take a problem-first approach rather than lead with AI solutions. Too many companies want to “do something with AI” without identifying what specific business problem they’re trying to solve, which usually leads to impressive demos that don’t move the needle.
My typical process follows three steps:
First, I focus on problem diagnosis. We identify specific pain points with measurable impact. For example, I recently worked with a client in the restaurant space facing slowing revenue growth. Instead of jumping to an “AI-powered solution,” we examined customer review data to identify patterns. For example, which menu items drove complaints, what service elements generated positive feedback, and which operational issues appeared most frequently. This data-driven diagnosis led to specific recommendations rather than generic AI implementations.
Second, we define success upfront. I insist on quantifiable metrics like time savings, quality improvements, or revenue increases. If we can’t measure it, we can’t prove it worked. This prevents scope creep and ensures we’re solving real problems, not just building cool technology.
Third, we go through viable solutions and align on the best one. Sometimes that’s a visualization dashboard, sometimes it’s a RAG system, sometimes it’s adding predictive capabilities. AI isn’t always the answer, but when it is, we know exactly why we’re using it and what success looks like.
This approach has delivered positive results. Clients typically see improved decision-making speed and clearer data insights. While I’m building my independent practice, focusing on real problems rather than AI buzzwords has been key to client satisfaction and repeat engagements.
You’ve mentored aspiring data scientists — what’s one common pitfall you see among people trying to break into the field, and how do you advise them to avoid it?
The biggest pitfall I see is trying to learn everything instead of focusing on one role. Many people, including myself early on, feel like they need to take every AI course and master every concept before they’re “qualified.”
The reality is that data science encompasses very different roles: from product data scientists running A/B tests to ML engineers deploying models in production. You don’t need to be an expert at everything.
My advice: Pick your lane first. Figure out which role excites you most, then focus on sharpening those core skills. I personally transitioned from analyst to ML engineer by intensely studying machine learning and taking on real projects (you can read my transition story here). I leveraged my domain expertise in credit and fraud risk, and applied this to feature engineering and business impact calculations.
The key is applying these skills to real problems, not getting stuck in tutorial hell. I see this pattern constantly through my newsletter and mentoring. People who break through are the ones who start building, even when they don’t feel ready.
The landscape of AI roles keeps evolving. How should newcomers decide where to focus — ML engineering, data analytics, LLMs, or something else entirely?
Start with your current skill set and what interests you, not what sounds most prestigious. I’ve worked across different roles (analyst, data scientist, ML engineer) and each brought valuable, transferable skills.
Here’s how I’d approach the decision:
If you’re coming from a business background: Product data scientist roles are often the easiest entry point. Focus on SQL, A/B testing, and data visualization skills. These roles often value business intuition over deep technical skills.
If you have programming experience: Consider ML engineering or AI engineering. The demand is high, and you can build on existing software development skills.
If you’re drawn to infrastructure: MLOps engineering is highly in demand, especially as more companies deploy ML and AI models at scale.
The landscape keeps evolving, but as mentioned above, domain expertise often matters more than following the latest trend. I won that ML competition because I understood credit risk fundamentals, not because I knew the fanciest algorithms.
Focus on solving real problems in domains you understand, then let the technical skills follow. To learn more about different roles, I’ve written about the 5 types of data science career paths here.
What’s one AI or data science topic you think more people should be writing about or one trend you’re watching closely right now?
I’ve been blown away by the speed and quality of text-to-speech (TTS) technology in mimicking real conversational patterns and tone. I think more people should be writing about TTS technology for endangered language preservation.
As a polyglot who’s passionate about cross-cultural understanding, I’m fascinated by how AI could help prevent languages from disappearing entirely. Most TTS development focuses on major languages with massive datasets, but there are over 7,000 languages worldwide, and many are at risk of extinction.
What excites me is the potential for AI to create voice synthesis for languages that might only have a few hundred speakers left. This is technology serving humanity and cultural preservation at its best! When a language dies, we lose unique ways of thinking about the world, specific knowledge systems, and cultural memory that can’t be translated.
The trend I’m watching closely is how transfer learning and voice cloning are making this technically feasible. We’re reaching a point where you might only need hours rather than thousands of hours of audio data to create quality TTS for new languages, especially using existing multilingual models. While this technology raises valid concerns about misuse, applications like language preservation show how we can use these capabilities responsibly for cultural good.
As I continue developing my language learning product and building my consulting practice, I’m constantly reminded that the most interesting AI applications often come from combining technical capabilities with deep domain understanding. Whether it’s building machine learning models or cultural communication tools, the magic happens at the intersection.
To learn more about Claudia‘s work and stay up-to-date with her latest articles, you can follow her on TDS, Substack, or Linkedin.
