Understanding the Career Trajectory of Data Scientists

Congratulations on landing your position as a data scientist! All those late nights refining your resume and preparing for interviews have finally borne fruit. But what comes next?

While the excitement of starting your career is undeniable, it's just the beginning of a lengthy journey. To navigate this path effectively, it's crucial to comprehend what it entails, manage your expectations, and identify areas for personal and professional growth.

So, what does the career ladder for a data scientist look like? What distinguishes a junior from a senior data scientist in a large organization? In this article, we will delve into these questions.

Before we proceed, a few clarifications: the insights shared here stem from my experiences in a consulting-focused team, which may not be representative of the entire industry. Additionally, this discussion will concentrate on those who prefer a technical career trajectory rather than moving into management roles.

To provide clarity, we will analyze the responsibilities and scope of data scientists at various levels across four dimensions:

1. Problem-solving
2. Communication
3. Relationship management
4. Work planning and delegation

Graduate Data Scientist

This entry-level position is typically occupied by recent university graduates with less than two years of experience, often part of a Graduate Program.

The primary objective for a Graduate Data Scientist is to absorb as much knowledge as possible while making meaningful contributions to the organization. Companies often assign a mentor to guide graduates and organize workshops on topics such as communication, data privacy, client interaction, and technical upskilling.

At my workplace, every graduate completes a proficiency checklist to monitor their learning and development in both technical and non-technical areas.

#### 1. Problem-solving

A Graduate Data Scientist should possess fundamental problem-solving abilities, such as breaking tasks into manageable steps and understanding prioritization. Daily tasks may include writing SQL queries, conducting analyses using Excel or R, creating data visualizations, developing dashboards, and assisting in preparing PowerPoint presentations for clients.

#### 2. Communication

In this role, you will learn to present findings in an accessible manner, utilizing tables or graphs. Ultimately, the effectiveness of an analysis hinges on how well the audience can grasp the key takeaways.

#### 3. Relationship Management

As a newcomer, a Graduate Data Scientist has minimal delegation or supervisory responsibilities. However, this is an opportune time to cultivate relationships with colleagues and client teams, which can facilitate smoother collaboration in the future.

#### 4. Work Planning and Delegation

At this stage, the focus is primarily on executing assigned tasks rather than delegating responsibilities.

Data Scientist

Upon completing the Graduate Program, you transition into the role of a Data Scientist, which most professionals at this level will occupy.

Data Scientists are actively involved in driving projects forward under the mentorship of senior colleagues while also overseeing the work of graduates and interns.

#### 1. Problem-solving

Data Scientists often tackle similar tasks as graduates but may take on more complex challenges, such as developing advanced models with guidance from senior data scientists. They also check the accuracy of results provided by juniors and recommend next steps based on their analyses.

#### 2. Communication

At this level, data scientists begin organizing team meetings and facilitating internal discussions. They might also lead knowledge-sharing sessions, teaching juniors how to use tools like Git effectively.

#### 3. Relationship Management

Supporting less experienced colleagues is a significant part of a Data Scientist's role. They must communicate effectively with senior data scientists about their progress and any challenges encountered.

#### 4. Work Planning and Delegation

While delegation remains limited, data scientists are expected to consistently deliver high-quality work on assigned tasks.

Senior Data Scientist

After successfully managing multiple projects and supporting junior data scientists, you may be promoted to Senior Data Scientist, marking your first step toward team and project management.

#### 1. Problem-solving

A Senior Data Scientist interprets client requests and defines problems at the project level, guiding junior data scientists to implement solutions effectively.

#### 2. Communication

They are responsible for drafting and presenting solutions to clients, usually through PowerPoint presentations.

#### 3. Relationship Management

Senior Data Scientists balance their time between mentoring juniors and relaying updates to stakeholders. They communicate team progress to clients and support their team members as needed.

#### 4. Work Planning and Delegation

At this stage, they are expected to plan workstreams and delegate tasks among junior team members while conducting regular check-ins to assess progress and adjust priorities.

Lead Data Scientist

With several years of experience in project and people management, you may advance to Lead Data Scientist, acting as the primary client contact for specific projects.

#### 1. Problem-solving

Lead Data Scientists define project agendas and collaborate with clients to outline problems and potential solutions while managing the analytical processes.

#### 2. Communication

They handle daily communications with clients and lead team meetings to provide updates on progress.

#### 3. Relationship Management

As the main client contact, Lead Data Scientists shape the project's culture and direction internally.

#### 4. Work Planning and Delegation

They divide projects into workstreams led by Senior Data Scientists, ensuring that tasks are allocated appropriately to meet timelines while fostering team development.

Principal Data Scientist

After serving as a Lead Data Scientist, professionals usually have two paths: transitioning into management roles or becoming technical experts. Here, we will focus on the latter— the role of a Principal Data Scientist.

#### 1. Problem-solving

Although not assigned to specific projects, Principal Data Scientists oversee quality control and best practices across all initiatives while also researching innovative technologies and algorithms.

#### 2. Communication

They quickly familiarize themselves with project needs and propose solutions alongside Lead Data Scientists.

#### 3. Relationship Management

Principal Data Scientists maintain strong connections with Lead Data Scientists, offering timely support when necessary.

#### 4. Work Planning and Delegation

While they do not delegate tasks, they must efficiently plan their schedules to assist project leads seeking guidance.

In conclusion, while job titles and responsibilities may vary across companies, I hope this article has provided valuable insights into the career progression of data scientists.

Don't know what to read next? Here are some suggestions.

The first video titled "What Is the Career Path for Data Scientists?" offers insights into various career trajectories within the data science field.

The second video, "What I actually do as a Data Scientist (salary, job, reality)," provides a realistic overview of the daily responsibilities and challenges faced by data scientists.