Within the Writer Highlight collection, TDS Editors chat with members of our group about their profession path in knowledge science and AI, their writing, and their sources of inspiration. At present, we’re thrilled to share our dialog with Mariya Mansurova.
Mariya’s story is certainly one of perpetual studying. Beginning with a powerful basis in software program engineering, arithmetic, and physics, she’s spent extra thanover 12 years constructing experience in product analytics throughout industries, from search engines like google and analytics platforms to fintech. Her distinctive path, together with hands-on expertise as a product supervisor, has given her a 360-degree view of how analytical groups might help companies make the correct selections.
Now serving as a Product Analytics Supervisor, she attracts power from discovering contemporary insights and revolutionary approaches. Every of her articles on In direction of Knowledge Science displays her newest “aha!” second: a testomony to her perception that curiosity drives actual progress.
You’ve written extensively about agentic AI and frameworks like smolagents and LangGraph. What excites you most about this rising house?
I first began exploring generative AI largely out of curiosity and, admittedly, a little bit of FOMO. Everybody round me gave the impression to be utilizing LLMs or at the very least speaking about them. So I carved out time to get hands-on, beginning with the very fundamentals like prompting methods and LLM APIs. And the deeper I went, the extra excited I turned.
What fascinates me probably the most is how agentic techniques are shaping the way in which we reside and work. I consider that this affect will solely proceed to develop over time. That’s why I exploit each probability to make use of agentic instruments like Copilot or Claude Desktop or construct my very own brokers utilizing applied sciences like smolagents, LangGraph or CrewAI.
Essentially the most impactful use case of Agentic AI for me has been coding. It’s genuinely spectacular how instruments like GitHub Copilot can enhance the pace and the standard of your work. Whereas recent research from METR has questioned whether or not the effectivity positive factors are actually that substantial, I undoubtedly discover a distinction in my day-to-day work. It’s particularly useful with repetitive duties (like pivoting tables in SQL) or when working with unfamiliar applied sciences (like constructing an online app in TypeScript). General, I’d estimate a couple of 20% enhance in pace. However this enhance isn’t nearly productiveness; it’s a paradigm shift that additionally expands what feels potential. I consider that as agentic instruments proceed to evolve, we are going to see a rising effectivity hole between people and corporations which have discovered find out how to leverage these applied sciences and those who haven’t.
On the subject of analytics, I’m particularly enthusiastic about automated reporting brokers. Think about an AI that may pull the correct knowledge, create visualisations, carry out root trigger evaluation the place wanted, observe open questions and even create the primary draft of the presentation. That may be simply magical. I’ve constructed a prototype that generates such KPI narratives. And though there’s a big hole between the prototype and a manufacturing answer that works reliably, I consider we are going to get there.
You’ve written three articles underneath the “Practical Computer Simulations for Product Analysts” collection. What impressed that collection, and the way do you assume simulation can reshape product analytics?
Simulation is a vastly underutilised instrument in product analytics. I wrote this collection to point out individuals how highly effective and accessible the simulations might be. In my day-to-day work, I maintain encountering what-if questions like “What number of operational brokers will we’d like if we add this KYC management?” or “What’s the possible impression of launching this characteristic in a brand new market?”. You’ll be able to simulate any system, regardless of how complicated. So, simulations gave me a approach to reply these questions quantitatively and pretty precisely, even when laborious knowledge wasn’t but obtainable. So I’m hoping extra analysts will begin utilizing this method.
Simulations additionally shine when working with uncertainty and distributions. Personally, I desire bootstrap strategies to memorising a protracted listing of statistical formulation and significance standards. Simulating the method typically feels extra intuitive, and it’s much less error-prone in observe.
Lastly, I discover it fascinating how applied sciences have modified the way in which we do issues. With at the moment’s computing energy, the place any laptop computer can run hundreds of simulations in minutes and even seconds, we will simply remedy issues that might have been difficult simply thirty years in the past. That’s a game-changer for analysts.
A number of of your posts deal with transitioning LLM functions from prototype to production. What widespread pitfalls do you see groups make throughout that section?
By way of observe, I’ve found there’s a big hole between LLM prototypes and manufacturing options that many groups underestimate. The commonest pitfall is treating prototypes as in the event that they’re already production-ready.
The prototype section might be deceptively clean. You’ll be able to construct one thing practical in an hour or two, take a look at it on a handful of examples, and really feel such as you’ve cracked the issue. Prototypes are nice instruments to show feasibility and get your group excited concerning the alternatives. However right here’s the place groups typically stumble: these early variations present no ensures round consistency, high quality, or security when dealing with numerous, real-world eventualities.
What I’ve discovered is that profitable manufacturing deployment begins with rigorous analysis. Earlier than scaling something, you want clear definitions of what “good efficiency” appears like when it comes to accuracy, tone of voice, pace and every other standards particular to your use case. Then you could monitor these metrics repeatedly as you iterate, guaranteeing you’re truly enhancing somewhat than simply altering issues.
Consider it like software program testing: you wouldn’t ship code with out correct testing, and LLM functions require the identical systematic method. This turns into particularly essential in regulated environments like fintech or healthcare, the place you could exhibit reliability not simply to your inner group however to compliance stakeholders as nicely.
In these regulated areas, you’ll want complete monitoring, human-in-the-loop assessment processes, and audit trails that may stand up to scrutiny. The infrastructure required to assist all of this typically takes way more improvement time than constructing the unique MVP. That’s one thing that persistently surprises groups who focus totally on the core performance.
Your articles typically mix engineering ideas with knowledge science/analytics greatest practices, reminiscent of your “Top 10 engineering lessons every data analyst should know.” Do you assume the road between knowledge and engineering is blurring?
The function of an information analyst or an information scientist at the moment typically requires a mixture of expertise from a number of disciplines.
- We write code, so we share widespread floor with software program engineers.
- We assist product groups assume via technique and make selections, so product administration expertise are helpful.
- We draw on statistics and knowledge science to construct rigorous and complete analyses.
- And to make our narratives compelling and truly affect selections, we have to grasp the artwork of communication and visualisation.
Personally, I used to be fortunate to achieve numerous programming expertise early on, again in school and college. This background helped me tremendously in analytics: it elevated my effectivity, helped me collaborate higher with engineers and taught me find out how to construct scalable and dependable options.
I strongly encourage analysts to undertake software program engineering greatest practices. Issues like model management techniques, testing and code assessment assist analytical groups to develop extra dependable processes and ship higher-quality outcomes. I don’t assume the road between knowledge and engineering is disappearing totally, however I do consider that analysts who embrace an engineering mindset shall be far simpler in fashionable knowledge groups.
You’ve explored each causal inference and cutting-edge LLM tuning methods. Do you see these as a part of a shared toolkit or separate mindsets?
That’s truly an ideal query. I’m a powerful believer that each one these instruments (from statistical strategies to fashionable ML methods) belong in a single toolkit. As Robert Heinlein famously stated, “Specialisation is for bugs.”
I consider analysts as knowledge wizards who assist their product groups remedy their issues utilizing no matter instruments match one of the best: whether or not it’s constructing an LLM-powered classifier for NPS feedback, utilizing causal inference to make strategic selections, or constructing an online app to automate workflows.
Somewhat than specialising in particular expertise, I desire to deal with the issue we’re fixing and maintain the toolset as broad as potential. This mindset not solely results in higher outcomes but in addition fosters a steady studying tradition, which is crucial in at the moment’s fast-moving knowledge business.
You’ve lined a broad vary of matters, from text embeddings and visualizations to simulation and multi AI agent. What writing behavior or guideline helps you retain your work so cohesive and approachable?
I often write about matters that excite me in the mean time, both as a result of I’ve simply discovered one thing new or had an attention-grabbing dialogue with colleagues. My inspiration typically comes from on-line programs, books or my day-to-day duties.
After I write, I all the time take into consideration my viewers and the way this piece might be genuinely useful each for others and for my future self. I attempt to clarify all of the ideas clearly and depart breadcrumbs for anybody who needs to dig deeper. Over time, my weblog has grow to be a private information base. I typically return to previous posts: typically simply to repeat a code snippet, typically to share a useful resource with a colleague who’s engaged on one thing related.
As everyone knows, all the things in knowledge is interconnected. Fixing a real-world downside typically requires a mixture of instruments and approaches. For instance, if you happen to’re estimating the impression of launching in a brand new market, you may use simulation for situation evaluation, LLMs to discover buyer expectations, and visualisation to current the ultimate suggestion.
I attempt to mirror these connections in my writing. Applied sciences evolve by constructing on earlier breakthroughs, and understanding the foundations helps you go deeper. That’s why a lot of my posts reference one another, letting readers observe their curiosity and uncover how completely different items match collectively.
Your articles are impressively structured, typically strolling readers from foundational ideas to superior implementations. What’s your course of for outlining a posh piece earlier than you begin writing?
I consider I developed this fashion of presenting info in class, as these habits have deep roots. Because the e-book The Tradition Map explains, completely different cultures range in how they construction communication. Some are concept-first (ranging from fundamentals and iteratively shifting to conclusions), whereas others are application-first (beginning with outcomes and diving deeper as wanted). I’ve undoubtedly internalised the concept-first method.
In observe, a lot of my articles are impressed by on-line programs. Whereas watching a course, I define the tough construction in parallel so I don’t neglect any necessary nuances. I additionally observe down something that’s unclear and mark it for future studying or experimentation.
After the course, I begin eager about find out how to apply this information to a sensible instance. I firmly consider you don’t actually perceive one thing till you strive it your self. Despite the fact that a lot of the programs have sensible examples, they’re typically too polished. So, solely whenever you apply the identical concepts on your personal use case will you run into edge circumstances and friction factors. For instance, the course may use OpenAI fashions, however I’d need to strive a neighborhood mannequin, or the default system immediate within the framework doesn’t work for my specific case and desires tweaking.
As soon as I’ve a working instance, I transfer to writing. I desire separate drafting from modifying. First, I deal with getting all my concepts and code down with out worrying about grammar or tone. Then I shift into modifying mode: refining the construction, choosing the proper visuals, placing collectively the introduction, and highlighting the important thing takeaways.
Lastly, I learn the entire thing end-to-end from the start to catch something I’ve missed. Then I ask my companion to assessment it. They typically carry a contemporary perspective and level out issues I didn’t take into account, which helps make the article extra complete and accessible.
To be taught extra about Mariya‘s work and keep up-to-date along with her newest articles, observe her right here on TDS and on LinkedIn.
