You latterly won $10,000 in a machine learning competition — congratulations! What was the largest lesson you took away from that have, and the way has it shaped your approach to real-world ML problems?
My biggest lesson was realizing that domain expertise matters greater 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 regular credit risk problem. This attitude proved more priceless 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 best than perfect. I spent only 10 hours on the competition and submitted an “MVP” approach somewhat than over-engineering it. This is applicable on to industry work: an honest model running in production delivers more value than a highly optimized model sitting in a Jupyter notebook.
Second, I discovered that the majority barriers are mental, not technical. I almost didn’t enter because I didn’t know Web3 or feel like a “competition person”, but looking back, I used to be overthinking it. While I’m still working on applying this lesson more broadly, it has modified how I evaluate opportunities. I now give attention to whether I understand the core problem and whether it excites me, and trust that I’ll have the ability to figure it out as I’m going.
Your profession 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 recent chapter? What sorts of challenges or clients are you most excited to work with?
The shift to independent work was driven by wanting to construct something I could truly own and grow. In corporate roles, you construct priceless systems that outlive your tenure, but you possibly can’t take them with you or get ongoing credit for his or her success. Winning this competition showed me I had the abilities to create my very own solutions somewhat than simply contributing to another person’s vision. I learned priceless skills in corporate roles, but I’m excited to use them to challenges I care deeply about.
I’m pursuing this through two principal paths: consulting projects that leverage my data science and machine learning expertise, and constructing 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 construct in public and sharing my journey through my newsletter.
As a polyglot who speaks nine languages, I’ve thought deeply concerning the challenges of achieving conversational fluency and not only 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 constructing AI solutions that have in mind cultural context and conversational nuance. On the consulting side, I’m energized by working with firms that want to resolve real problems somewhat than simply implementing AI for the sake of getting AI. Whether it’s working on risk models or streamlining information retrieval, I really like projects where domain expertise and practical AI intersect.
Many firms are desperate to “do something with AI” but don’t all the time know where to start out. What’s your typical process for helping a brand new client scope and prioritize their first AI initiative?
I take a problem-first approach somewhat than lead with AI solutions. Too many firms need to “do something with AI” without identifying what specific business problem they’re trying to resolve, which often results in impressive demos that don’t move the needle.
My typical process follows three steps:
First, I give attention to problem diagnosis. We discover specific pain points with measurable impact. For instance, I recently worked with a client within the restaurant space facing slowing revenue growth. As an alternative of jumping to an “AI-powered solution,” we examined customer review data to discover patterns. For instance, which menu items drove complaints, what service elements generated positive feedback, and which operational issues appeared most steadily. This data-driven diagnosis led to specific recommendations somewhat than generic AI implementations.
Second, we define success upfront. I insist on quantifiable metrics like time savings, quality improvements, or revenue increases. If we will’t measure it, we will’t prove it worked. This prevents scope creep and ensures we’re solving real problems, not only constructing cool technology.
Third, we undergo viable solutions and align on one of the best one. Sometimes that’s a visualization dashboard, sometimes it’s a RAG system, sometimes it’s adding predictive capabilities. AI isn’t all the time the reply, but when it’s, we all 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 constructing my independent practice, specializing in real problems somewhat 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 amongst people attempting to break into the sector, and the way do you advise them to avoid it?
The most important pitfall I see is attempting to learn every little thing as a substitute of specializing in one role. Many individuals, including myself early on, feel like they should take every AI course and master every concept before they’re “qualified.”
The truth 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 should be an authority at every little thing.
My advice: Pick your lane first. Work out which role excites you most, then give attention to sharpening those core skills. I personally transitioned from analyst to ML engineer by intensely studying machine learning and taking up real projects (you possibly 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 bottom line is applying these skills to real problems, not getting stuck in tutorial hell. I see this pattern always through my newsletter and mentoring. Individuals who break through are those who start constructing, even after they don’t feel ready.
The landscape of AI roles keeps evolving. How should newcomers determine where to focus — ML engineering, data analytics, LLMs, or something else entirely?
Start along 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 every brought priceless, transferable skills.
Here’s how I’d approach the choice:
In case you’re coming from a business background: Product data scientist roles are sometimes the best entry point. Deal with SQL, A/B testing, and data visualization skills. These roles often value business intuition over deep technical skills.
If you might have programming experience: Consider ML engineering or AI engineering. The demand is high, and you possibly can construct on existing software development skills.
In case you’re drawn to infrastructure: MLOps engineering is very in demand, especially as more firms deploy ML and AI models at scale.
The landscape keeps evolving, but as mentioned above, domain expertise often matters greater than following the newest trend. I won that ML competition because I understood credit risk fundamentals, not because I knew the fanciest algorithms.
Deal with solving real problems in domains you understand, then let the technical skills follow. To learn more about different roles, I’ve written concerning the 5 forms of data science profession paths here.
What’s one AI or data science topic you think that more people must be writing about or one trend you’re watching closely right away?
I’ve been blown away by the speed and quality of text-to-speech (TTS) technology in mimicking real conversational patterns and tone. I believe more people must be writing about TTS technology for endangered language preservation.
As a polyglot who’s captivated with 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 lots of are vulnerable to extinction.
What excites me is the potential for AI to create voice synthesis for languages which may only have a couple of hundred speakers left. That is technology serving humanity and cultural preservation at its best! When a language dies, we lose unique ways of fascinated by the world, specific knowledge systems, and cultural memory that may’t be translated.
The trend I’m watching closely is how transfer learning and voice cloning are making this technically feasible. We’re reaching some extent where you may only need hours somewhat than hundreds of hours of audio data to create quality TTS for brand spanking new languages, especially using existing multilingual models. While this technology raises valid concerns about misuse, applications like language preservation show how we will use these capabilities responsibly for cultural good.
As I proceed developing my language learning product and constructing my consulting practice, I’m always reminded that probably the most interesting AI applications often come from combining technical capabilities with deep domain understanding. Whether it’s constructing machine learning models or cultural communication tools, the magic happens on the intersection.
To learn more about Claudia‘s work and stay up-to-date together with her latest articles, you possibly can follow her on TDS, Substack, or Linkedin.
