Finding Your Niche: Unveiling the Seven Archetypes of Data Science Careers

Data science is a diverse field, and the roles within it can be quite different from one another. This post outlines various archetypes of data science roles, shedding light on the day-to-day tasks associated with each. Understanding these archetypes can help data professionals find the right fit for their skills and preferences.

1. The Analytics Guru

This underrated and under-appreciated role revolves around helping the company measure goals and assess performance. Key tasks may include building dashboards, analyzing sales and product success, and evaluating internal performance metrics. Analytics Gurus handle complex data from various sources and rely heavily on SQL for their day-to-day tasks, often focusing more on analytics than machine learning.

2. The Feature Builder

The Feature Builder's primary goal is to improve the product using data science and machine learning techniques. These professionals need to understand the target customers to create genuinely useful features, not just incorporate the latest AI buzzword into the product. Collaboration is critical; Feature Builders often work with The Analytics Guru to evaluate their work and ensure it aligns with business goals.

3. The Infra Builder

Also known as the ML Engineer, Infra Builders create the infrastructure that makes ML models and data science features usable in a product. Their focus is on scaling and optimizing data pipelines to minimize latency and ensure seamless integration. Infra Builders often work with Docker, product-specific languages, and Python to connect models crafted by Feature Builders to the end product.

4. The Internal Only

This role is more common in larger companies and involves creating ML tools for internal use by various departments. Their goal is to improve internal processes and make employees' lives easier by building models or automating repetitive tasks. Collaboration is crucial, as Internal Only data scientists must have an intimate understanding of the company org chart and employees' needs.

5. The Researcher

Hired to conduct pure research, Researchers delve into cutting-edge ideas and technologies that could shape the industry's future. Their output may include scholarly articles or proof-of-concept projects to advance the company's vision. This is the only role that might justifiably seek candidates with a Ph.D.

6. The Solutions Engineer

Solutions Engineers build data-driven features for customers rather than their own company. This role is prevalent in data science consulting or firms selling data-related software. Expect to interact with customers regularly, providing them with customized solutions, and answering technical questions about the product offered.

7. The Everything to Everyone

Sometimes, a company might seek a candidate capable of handling multiple roles described above, either due to a lack of awareness or a desire to save on costs. These roles often demand a wide range of skills and responsibilities that may not align with a single person's capabilities.

By understanding these archetypes, you can better navigate job postings, identify suitable roles, and find your ideal career path in the data science landscape. Happy job hunting, fellow data professionals!