Career path as a Data Scientist

Do you want to become a data scientist?

Data science is hot, whether you are already a data scientist or you plan on becoming one, it is important to build a career plan.

Usually, as in software engineering, data scientists can follow two paths to grow their career: the individual contributor (IC) or management path.

Data science is also a technical field, individual contributors tend to report to someone within an organization but they don't have any management responsibilities. As an Individual contributor, your main objective will be to contribute independently to your organization's missions and goals. This independence means that an IC has generally more time to become an expert in his/her field. In order to succeed, you will need to have the following skills: communication, time-management, rigor, organization, autonomy, etc.

Managers have a different role, they are expected to be in charge of a group of people, but also ensure that projects and processes are successfully implemented. Managers are also asked to attend strategic, budgetary and employee relations meeting. As a manager, you will have to delegate tasks to your direct reports. A good manager is someone who can delegate responsibilities, anticipate, invest in relationships and work strategically.

Now, let's dive in and see how your career as a data scientist could look like!

Individual Contributor Path

• Data Scientist
• Senior/Lead Data Scientist
• Staff/Principal Data Scientist
  

• Collecting, cleaning and manipulating data to meet their company's goals.
• Conducting research to develop statistical learning models for data analysis
• Collaborating with product management and engineering departments to suggest new solutions to improve one product or meet company's objectives (i.e increase conversion rate)
• Being able to communicate results and ideas to key stakeholders

• Being proactive by suggesting, formulating and managing data-driven projects to satisfy business' interests
• Cleaning data and creating pipelines from various data sources to increase productivity of junior data scientists
• Providing guidance to junior data scientists
• Keeping updated with latest trends in data science to ensure that methods used in his/her team are still relevant
• Having an extensive experience in SQL, Python (or R) with supervision and mentorship abilities
  

• Being responsible for the design, development and delivery of machine learning models
• Being seen as a data science lead that will define the technical roadmap and work with various teams
• Delivering projects successfully and driving initiatives to scale internal models
• Leading the development of data science practice within their company
• Hiring and coaching data scientist on the team

Management Path

• Manager of Data Science
• Director of Data Science
• VP of Data Science

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Manager of Data Science

• Managing a team of data scientists and producing project deliverables
  
• Motivating data scientists to deliver projects efficiently
• Presenting analytical finds to senior management and external stakeholders
• Identifying and driving initiatives that will maximize the business impact
• Having great communication skills

Director of Data Science

• Shaping the data strategy and establishing KPIs and success metrics
  
• Developing the team's culture and hiring standards
  
• Partnering up with senior management to define the Data science roadmap
  
• Executing his/her duties and duties delegated by the Chief Data Officer or VP of Data Science

VP of Data Science

• Being the voice of Data Science internally
  
• Collaborating on short and long-term projects with cross-functional partners in product, engineering, operations, finance, and marketing
• Ensuring data best practices and helping create a data-informed culture
• Supporting and promoting data-informed decision making in the company