Progression Of A Data Scientist

Sequoia Capital Publication
7 min readMay 16, 2019


In past posts, we have discussed the building blocks of a data-informed company; how to build world class teams; the evolution and characteristics of a data organization; the value of data science; and the roles and skills of data scientists. In this post, we discuss the career progression of a data scientist. Specifically, we examine what characteristics senior product data scientists have relative to junior ones and why a healthy data-informed company should invest in the development of its data scientists.


“If we want to have the biggest impact, the best way to do this is to make sure we always focus on solving the most important problems. It sounds simple, but we think most companies do this poorly and waste a lot of time. We expect everyone at Facebook to be good at finding the biggest problems to work on.” — Mark Zuckerberg

Data science is a scientific and truth-seeking discipline that uses data to extract knowledge and insights. A product data scientist focuses on setting goals and informing product roadmaps and strategies. They improve products by evaluating and understanding product health, identifying opportunities and issues, and providing data-based recommendations and solutions.

The best data scientists are the ones who focus relentlessly on impact — usually be measured by moving a metric and influencing a product or process. As they progress in their careers, data scientists should increase their scope of impact. This greater impact can be achieved through four levels of increasing scope: Project, Product, Domain, and Company.


To advance along these dimensions, data scientists need to master five core skills and abilities: problem formulation, technical ability, analytical ability, synthesis, and influence.

  1. Problem formulation: Data scientists must be able to formulate and structure problems. This generally requires a consulting mindset as well as a scientific approach to problem-solving.
  2. Technical ability: Programming and scientific skills are both required to extract data.
  3. Analytical ability: Analytical skills are required to extract and manipulate data sets, and to extract value from the data in the form of tables, charts, etc. A consulting mindset and scientific approach to problem-solving are necessary to make sense of the data.
  4. Synthesis: Data scientists need to interpret the results of their analyses, and then simplify and synthesize them. A consulting mindset is crucial for simplification and synthesis.
  5. Influence: Influencing decisions requires the ability to use data to tell a clear, compelling story. This requires a consulting mindset.


Data scientists at earlier stages of their career have the greatest impact on specific projects. As they progress, their impact grows from impacting a specific product to an entire domain. The most senior data scientists impact the entire company.

At different stages of their careers, data scientists have varying levels of proficiency within each of these five core skills and abilities. As a result, more junior data scientists require support and assistance from senior data scientists (as well as their managers) to ensure excellence. Generally speaking, the more senior a data scientist, the greater an impact they have.

The most junior data scientists (first level) have limited experience and focus primarily on execution. They may need substantial help across the five core skills and abilities. As they practice execution and reach higher levels of proficiency, they will be able to produce high-quality work more rapidly.

A second-level junior data scientist is more independent, especially in writing code and performing analyses. However, they typically need problems to be structured for them and require help in effectively influencing others to maximize impact.

As junior data scientists advance to the third level, they are able to formulate unstructured problems and identify the highest-impact problems, which they can then solve on their own.

At the fourth level, junior data scientists reach full independence. They can develop projects and complete them entirely on their own. They require little to no supervision, can prioritize their own work, and assist other team members. They become experts in a particular domain (e.g., ads, payments), can provide strategic direction within that area, and are able to help scale the organization. The primary difference between a junior and a senior data scientist is that they can work across multiple domains.

At the fifth level, junior data scientists transition into senior data scientists. For example, a junior data scientists could be an expert in payment-related risk but has not yet helped drive insights in a related field, say spam. The level five data scientist is able to drive use their risk analytics expertise to help multiple adjacent areas across the company. Thus, the senior data scientist is able to provide greater scale and efficiency through increased levels of functional leadership and has established themselves as a functional expert and leader.

As a senior data scientist transitions to the sixth level, a senior data scientist is able to have an impact across most problems at the company level, including the company’s hardest problems. They wear two hats, becoming product experts in addition to functional experts. This means that they provide direction on both product and function.

The seventh level is the apex of a data scientist’s career trajectory. At this stage, they are able to influence and execute the highest-level roadmap and strategy decisions at the company level, driving company and product transformations.

The escalation of scope and impact is common across different disciplines. In an engineering organization, the principle difference between a junior and senior developer is not that the code the senior developer commits is “better.” They are differentiated in the amount and type of assistance the junior developer needs to get to production quality code, and the scope of impact that this code may have. Similarly, all analytical work should be “production” quality, but junior product data scientists may require more help to get there and work their way up to the scope of impact senior analysts have.


Here are some of the most common questions we hear about the career trajectory of data scientists:

  1. What does the progress of a data scientist depend on? The short answer is impact. The larger the scope of impact a data scientist has, the greater their progress. The scope of impact depends chiefly on two things: focus area and surface area.
  2. Focus area: Not all areas have the same potential for impact. Working on a key company priority is usually more impactful than working on a peripheral support product.
  3. Surface area: Some analyses have wider ranges of impact than others. For example, an analysis that shows a decline in active users of Snapchat because of a product change is more impactful than changing the color scheme of a feature.
  4. What are the characteristics of different levels of data scientists?
  5. Autonomy and ownership increase with progression. As a data scientist becomes more senior, their independence increases, they take on greater ownership, and they consistently drive positive change.
  6. High-quality work occurs across all levels. The work quality is the same across all levels of the data scientist., but a junior data scientist may require more help than a senior one to have the same impact.
  7. Impact and improvement create career progression. Data scientists sometimes feel like they should be promoted regularly over time. But progress does not occur just by biding one’s time; it happens with improved abilities and increased impact.
  8. What processes are needed for people to progress? For data scientists to progress through the levels we outline, both the organization and the individual need to have a strong focus on learning and development. Learning and development should cater to the specific needs of the individual and correspond to their strengths and weaknesses across the five core skills and abilities of data scientists. Additionally, to expand a data scientist’s impact, coaching and mentorship should be provided on organizational and product leadership, driving excellence, prioritization, and building a data-informed culture.
  9. How does a data scientist progress to different levels?
  10. By deepening expertise in a single area. Advancing an understanding of one subject area (say, fraud analytics) helps to create a greater impact. With this increased expertise, a data scientist should be able to create frameworks that can help deepen understanding and drive scale. An ability to create these frameworks will help the data scientist progress.
  11. By solving harder problems. Solving the hardest problems requires breaking through barriers in understanding, creativity, and innovation. These breakthroughs typically lead to greater impact. The ability to innovate across multiple hard problems advances a data scientist’s career.
  12. By being outcome focused. Many data scientists produce good analyses but are not outcome focused. As a result, they are unable to change the course of a product. The best data scientists are able to drive a new strategy or come up with a roadmap — the necessary ingredient for both being outcome focused.
  13. By becoming an A+ player. A+ players have a strong growth mindset and are able to learn and grow across a role’s required dimensions faster than others.


  • Data scientists advance in their careers as they increase their ability to have an impact at the project, product, domain, and company levels.
  • Progress also happens when data scientists improve at the five core skills: abilities of problem formulation, technical ability, analytical ability, synthesis, and influence.



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