7 Assets Established Professionals Bring to Data Science

career advice job + productivity advice technical skill Jan 11, 2024

Established professionals transitioning into the field of data science, machine learning, artificial intelligence, or advanced analytics, bring a wealth of skills and experience that are invaluable to the discipline. This blog post aims to highlight seven crucial assets that such professionals contribute to their new roles. You are more ready than you may think, and these assets underline that fact.

1) Administrative Experience

  • This refers to fundamental skills often mastered by established professionals, such as document filing, note-taking, and managing multiple schedules.
  • Attributes include multitasking, proactive team contributions, and effective time management.
  • This experience forms a solid foundation for organizational tasks in data science, aiding in project management, data organization, and efficient scheduling.

2) Management Experience

  • Management of both people and "things" (e.g., projects or resources) is a characteristic of many mid- and late-career professionals.
  • Experience in areas like budgeting, resource allocation, and team building are crucial in managing data science projects.
  • They are skilled in handling change, a necessity in the rapidly evolving data science landscape.

3) Supervisory Experience

  • Supervisory experience often involves being responsible for the work output of other professionals.
  • Mid- and late-career professionals usually possess the ability to provide constructive feedback, evaluate performance, manage conflict, and resolve work-related disputes.
  • In a data science team, these skills enable efficient task management, conflict resolution, and overall team success.

4) Strategic Experience

  • This involves aligning individual work, team goals, and organizational objectives – a skill mid- and late-career professionals often possess.
  • Their mindset usually focuses on the organization’s core purposes, efficiencies, and inefficiencies.
  • In data science, strategic thinkers are key to project planning, optimizing processes, and improving customer and employee experiences.

5) Leadership Experience

  • Mid- and late-career professionals generally have an abundance of leadership experience, fostering a motivating and inclusive atmosphere.
  • They understand the dynamics of decision-making, a critical attribute for driving data science projects.
  • This leadership often extends beyond the workplace into community and civic roles, indicating versatility and a wider range of experiences.

6) Professional Network

  • A vast professional network, built over years, provides a significant resource for business and personal assistance, information, and opportunities.
  • A well-maintained professional network can be a significant asset to a data science team or project, opening up resources and collaborations.
  • Importantly, this network can be leveraged for knowledge sharing, hiring, and even project collaborations in data science.

7) Credibility

  • Professional credibility, built over years, is another significant asset.
  • Credibility involves being trustworthy, competent, respectful, and accountable – essential qualities for any role, including data science.
  • Such credibility can enhance the reputation of a data science team or project, paving the way for future collaborations and opportunities.


In conclusion, the wealth of experience, skills, and credibility that mid- and late-career professionals bring to data science roles is substantial. Embracing a new career path in data science doesn't mean starting from scratch. Rather, it's a new chapter where past experiences play a vital role. Remember, everyone has to start someplace, and with your background and these seven assets, you are more ready than you may think.

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