An Overview of Data Scientist Archetypes in the AI Era

In a previous article, we discussed how data scientist interviews are rapidly evolving to assess skills that are relevant for data scientists to have in the AI-era. These changes in hiring practices are a direct reflection of the changes taking place internally at big tech companies, where the role of a data scientist is shifting from someone who simply leverages statistics/machine learning to turn data into actionable insights to someone who can design, build, and deploy AI-powered systems, integrate large language models into products, and drive business impact through end-to-end ownership of intelligent applications.

This change is most clearly reflected in the new "DS archetypes" that have arisen over the past two years. Traditionally, the three main DS archetypes at big tech companies included:

• "Domain Expert": someone who has a deep understanding of a specific product area, knows the key metrics, tradeoffs, and user behavior in that area, and can influence the team roadmap and strategy through insights.
• "Technical Guru": someone who is an expert at statistics, ML, causal inference, and/or experimentation, builds reusable frameworks and tools, and is often consulted by other DSs for difficult methodological questions.
• "Product Generalist": someone who operates almost like a PM and is focused on influencing direction, shaping prioritization and business impact, and frequently presents to directors and VPs.

With the advent of AI tooling, these archetypes are no longer as relevant in their traditional forms. Instead, across the industry, we're seeing a transformation of these three data scientist archetypes into the following AI-native ones: (1) "AI Enabler", (2) "Insights Specialist", and (3) "Product Expert". Below we'll cover each of these archetypes in detail and what they mean for aspiring data scientists and those already in the industry today.

"AI Enabler" - Enable Organizations to 10X with AI

The "AI Enabler" archetype pertains to data scientists that work to build large-scale solutions for autonomous AI agents and XFN partners to be able to reason accurately and operate reliably in their domain. These data scientists focus on:

1. Pushing the frontier forward for agentic AI adoption and improving AI effectiveness.
2. Understanding which foundational data investments unlock the greatest step-change in agent performance.
3. Designing large-scale, robust data systems for agent consumption and continuous iterative improvement.
4. Investing in rigorous evaluation methodologies of AI agent/tool performance and accuracy.

Aspiring/industry data scientists looking to fall into this archetype should focus on using AI tools deeply and comparatively to understand the advantages and drawbacks of each. Furthermore, they should understand what holds back XFN partners from AI adoption and build tools to fix these blockers.

"Insights Specialist" - Derive 10X Insights with AI

The "Insights Specialist" archetype pertains to data scientists that are focused on leveraging the speed/efficiency gains from AI across a broad remit of analytical tasks that would previously be solved manually. These include:

1. Being AI power-users to unlock 10X productivity gains.
2. Operating across the data stack to develop logging, build data pipelines/infra, perform data analysis, and produce end-to-end solutions.
3. Solving analytics problems using AI that can't be solved by generalists due to their inherent difficulty/ambiguity.

Aspiring data scientists looking to fall into this archetype should use AI tools to improve their own efficiency, productivity, and story-telling. Furthermore, they should use AI tools in ways to unlock end to end ownership of a particular product, rather than only touching one area of the stack.

"Product Expert" - Create 10X Product Impact with AI

The "Product Expert" archetype pertains to data scientists that are leveraging AI to own E2E problems and are often directly shipping product changes (i.e writing production code) themselves. Some examples of this include:

1. Taking product ideas and executing on them from start to finish themselves.
2. Indexing towards a variety of E2E ownership cycles such as "insight -> design -> shipping code" or "setting strategy with data -> defining roadmaps -> partnering with engineers to ship".
3. Partnering with PM, design, UXR, and engineering to achieve functional quality standards.

Aspiring data scientists looking to fall into this archetype should use AI to solve end to end problems themselves and to learn the fundamentals to be a responsible builder and produce code with high quality.

Closing Thoughts

The data scientist role is quickly changing with the advent of AI, and while it's still uncertain exactly what the future will hold, there are early tell-tale signs on where things are heading based on how the day-to-day DS work is evolving. The archetypes presented in this article give a preview into where data scientists can land the most impact in their roles and how companies are beginning to view data scientists value in the AI-era.