What are the levels of a data scientist?

The Four Career Levels of a Data Scientist:

1. Level 1 - Associate or Junior Data Scientist: A junior data scientist is an entry-level position. They work under the mentorship of Level 2 and 3 data scientists testing new ideas, debugging, and refactoring existing models. A Level 1 data scientist should be skilled in Python, R, research, SQL, and data analysis.

   A junior data scientist often assists in preparing large structured and unstructured datasets for analysis, applies basic principles of statistics and algorithms to analyze the data, and presents findings to the data science team. They may also be tasked with keeping up-to-date with emerging trends and advancements in the field.

2. Level 2 - Data Scientists: This level is achieved typically after 1 to 3 years of experience as a Level 1. A Level 2 data scientist should be an expert in Python, R, research, SQL, data analysis, machine learning, analytical skills, teamwork, and communication skills.

   At this level, a data scientist not only analyzes and interprets complex digital data but also designs new processes and algorithms for data modeling. They are responsible for communicating findings to both technology leaders and business managers to influence strategic decisions. They may also be asked to mentor junior team members.

3. Level 3 - Senior Data Scientists: Level is typically achieved after 3 to 5 years as a Level 2. A Level 3 should not just be tech-savvy and business-savvy but also understand the overall business picture, have expertise on the best types of data analytics technologies, prevent fraud, and maintain budgets.

   In addition to having a strong grasp on machine learning, a senior data scientist is expected to formulate and lead guided, multifaceted analytic studies against large volumes of data. They often act as an advisor to management and stakeholders on the potential applications and limitations of data analysis.

4. Level 4 - Principal Data Scientist: This position is the most experienced member of the data science team with ten or more years of data science experience and is considered the final authority for all other levels of data scientists.

   Principal data scientists are responsible for setting the direction of their organization's data analytics strategy. In addition to their technical skills, they must possess strong leadership abilities. This role is often heavily involved in project management, strategic decision-making, hiring, mentoring, and long-term planning.

The levels of a data scientist are a reflection of experience, skills, and scope of responsibility. As you move from a junior position to senior and principal roles, the job requires more advanced technical skills, a deeper understanding of the business domain, managerial abilities, and strategic thinking.