By Saeed Mirshekari

Nov 22, 2023

In the vast ocean of data science careers, professionals often find themselves at a crossroads, contemplating whether to be a small fish in a big tank or a big fish in a small tank. The choice between working in small, mid-size, or large companies can significantly impact one's career trajectory, presenting distinct differences, challenges, and opportunities. In this deep dive, we explore the intricacies of data science roles in different company sizes, delving into the reasons behind professionals' moves and the evolving landscape of opportunities in each.

The Small Pond: Thriving as a Big Fish in a Small Tank

Characteristics of Small Companies:

Small companies, often startups or niche players, provide an intimate setting where each individual's impact is palpable. In the realm of data science, being a big fish in a small tank signifies more than just a job title; it's a testament to the breadth of responsibilities one can shoulder.


  1. Diverse Roles: In small companies, data scientists often wear multiple hats. They might be involved in data engineering, analysis, and strategy, gaining a holistic understanding of the data science lifecycle.
  2. Quick Decision-Making: Smaller teams mean faster decision-making processes. Data scientists can see the direct impact of their work and contribute to shaping the company's data strategy.
  3. Close Collaboration: Proximity to other departments fosters collaboration. Data scientists in small companies often work closely with business stakeholders, bridging the gap between technical and non-technical teams.


  1. Limited Resources: Small companies may lack the resources and infrastructure of their larger counterparts, posing challenges for data scientists in terms of access to advanced tools and technologies.
  2. Niche Focus: While small companies offer a close-knit environment, the focus might be niche, limiting exposure to a broader range of data science applications.
  3. Risk and Stability: The dynamism of startups comes with inherent risks. Professionals must weigh the excitement of innovation against the stability offered by larger organizations.

Reasons for Moving:

  • Desire for Impact: Data scientists seeking a direct and visible impact often move to smaller companies, where their contributions shape the organization's data narrative.
  • Varied Responsibilities: Professionals craving diversity in their roles and the chance to work on a multitude of projects might find small companies appealing.
  • Startup Culture: The allure of startup culture, with its emphasis on agility and innovation, can be a driving force for those seeking a dynamic work environment.

The Mid-size Haven: Striking a Balance

Characteristics of Mid-size Companies:

Mid-size companies, balancing the intimacy of small firms with the resources of larger enterprises, offer a middle ground. Here, data scientists can still make a significant impact while enjoying the benefits of a more established structure.


  1. Structured Environment: Mid-size companies often have more structured processes and resources compared to startups, providing a stable platform for data scientists to thrive.
  2. Broader Exposure: The scope of projects in mid-size companies tends to be broader, allowing data scientists to explore various facets of their field.
  3. Growth Opportunities: Professionals can experience career growth as mid-size companies expand, offering leadership roles and opportunities for skill development.


  1. Balancing Act: Data scientists may face the challenge of balancing the need for agility and innovation with the growing structures inherent in mid-size companies.
  2. Competition: With a larger team, there may be more competition for projects and recognition, requiring individuals to stand out in a more crowded space.
  3. Resource Constraints: While mid-size companies have more resources than small startups, they might still face limitations compared to larger enterprises.

Reasons for Moving:

  • Stability and Growth: Data scientists seeking a balance between stability and growth often find mid-size companies appealing, offering a stable environment with room for career advancement.
  • Varied Projects: Professionals wanting exposure to a diverse range of projects without the constraints of a niche focus might make the move to mid-size companies.
  • Entrepreneurial Spirit: Individuals with an entrepreneurial spirit may appreciate the balance between structure and flexibility that mid-size companies often provide.

The Vast Ocean: Swimming as a Small Fish in a Big Tank

Characteristics of Large Companies:

Large corporations, with their expansive resources and global reach, create a different ecosystem for data scientists. Here, professionals can specialize in specific domains and work on complex, large-scale projects.


  1. Advanced Resources: Large companies boast advanced tools, technologies, and resources, providing data scientists with the infrastructure to tackle complex challenges.
  2. Specialization: Professionals can specialize in niche areas within data science, working on specialized projects that may not be feasible in smaller settings.
  3. Global Impact: The scale of operations in large companies allows data scientists to contribute to projects with a global impact, influencing the direction of the entire organization.


  1. Bureaucracy: The size and structure of large companies may introduce bureaucratic processes, potentially slowing down decision-making.
  2. Limited Visibility: Individual contributions may be less visible in a large organization, with projects involving larger teams and hierarchies.
  3. Specialization Pressure: While specialization is an opportunity, it may also create pressure for data scientists to become highly specialized, potentially limiting their overall skill set.

Reasons for Moving:

  • Access to Resources: Professionals seeking access to cutting-edge resources and technologies often move to large companies where infrastructure is robust.
  • Global Exposure: Data scientists desiring a global impact and the opportunity to work on large-scale projects may find large corporations appealing.
  • Specialization Goals: Individuals looking to specialize in a specific area within data science may make the move to large companies to focus on niche domains.

Navigating the Currents: Reasons for Transition

Seeking Impact:

  • Small to Mid-size: Individuals seeking a more direct and visible impact on projects often transition from large corporations to smaller or mid-size companies.
  • Mid-size to Large: Conversely, professionals moving from mid-size to large companies may be motivated by the desire to contribute to projects with a broader global impact.

Skill Development:

  • Small to Large: Data scientists may move from small companies to larger ones to access advanced tools and technologies, enhancing their skill set.
  • Large to Small: Conversely, those seeking a diverse skill set and the opportunity to work on various aspects of data science might transition from large corporations to smaller settings.

Career Growth:

  • Mid-size to Large: Professionals aiming for accelerated career growth may move from mid-size companies to large corporations, where leadership opportunities abound.
  • Large to Small: On the flip side, individuals seeking leadership roles with a more direct impact on strategy and decision-making might transition from large to smaller companies.

Conclusion: Navigating the Waters of Data Science Careers

In the vast ocean of data science roles, the decision to be a small fish in a big tank or a big fish in a small tank is deeply personal. Each choice comes with its unique set of challenges and opportunities, and professionals often find themselves navigating these waters to align with their career goals and aspirations. Whether driven by the desire for impact, skill development, or career growth, the evolving landscape of data science roles in small, mid-size, and large companies offers a myriad of possibilities for those ready to dive into the currents of this dynamic field.

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About O'Fallon Labs

In O'Fallon Labs we help recent graduates and professionals to get started and thrive in their Data Science careers via 1:1 mentoring and more.

Saeed Mirshekari, PhD

Saeed is currently a Director of Data Science in Mastercard and the Founder & Director of O'Fallon Labs LLC. He is a former research scholar at LIGO team (Physics Nobel Prize of 2017). Learn more about Saeed...

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