Gone and vanishing are the days that data enthusiasts occupy dark quarters, not seeing the light of day or speak a cryptic language. Data science, the current fancy term for data analytics, mining, and predictive algorithms are giving data nerds more options. But even if data scientists are highly desired, where should they actually work and where can they thrive in the workplace?
A Happy Data Scientist is a Better Data Scientist.
Working environments shape adults’ work relationships, leadership approaches, and communication styles. We spend more than a third of our life times in organizations, working and interacting with each other positively and negatively. Extending the “nature versus nurture,” the place of your work has an ability to further mold and change the adults mind. We pick up habits and learn new sensory responses to stress, rewards, and punishments. How we see the world into our old age can grow from work place interactions. It is just work, some times, but let’s not downplay it’s impact on your personality, optimism, and overall life trajectory.
Becoming attuned to “best fit” within organizational cultures requires a significant amount of intrapersonal and interpersonal development and awareness. The initial requirement is to understand your own core values, then learning to articulate and identify them in a team or organization. Organizations are a living organisms and are the sum of its parts. It is not easy to decipher the impact of one more person.
“Data science” is the hot phrase of the moment. The formation and dissemination of the terminology invokes many underlying fields and formally disparate professions. These related fields and professions within data science, such as statistician, database programmer, and business intelligence analyst, have existed eons before the onset of this trend. The bringing together of right and left brain skills can be a benefit or disservice, a topic not visited in this post. Regardless, if you are new to the field, an adaptive technical person, or seasoned professional, we can agree this is an exciting time for the field. So, then the question is how to make the best this opportunity, a sort of carpe diem for data nerds? This post will explore the characteristics of work environments that would optimize the aspirations and aptitude of a data scientists or data science team.
Below are elements to consider in organizational culture in your current or next role:
What is their data science story? Find out how data science fits into their business plan and vision for the organization.
Is the organization open and flexible to change? More likely than not, if an organization wants a data scientist, then it wants to change and innovate their business or product lines. But there is a fine line between desire and actualization. They may not have a proven track record yet in listening to or adopting new ideas but the same questions apply:
- Where do new ideas come from in your organizations?
- What are the general project/product constraints and who approves of these constraints?
- Client or products environment? Who sets the scope and constraints as well as review and approve your work?
Are you building new processes or maintaining existing ones?
- Are you asked to be an entrepreneur (‘disrupting’ from the outside) or intrapreneur (‘changing from the inside’)?
- What roles and departments does data analysis have to empower? This will dictate the type and level of communications expected with the organization.
How innovative or creative is the organization or team? Is the decision-making process already steeped with processes and paperwork. Find out with more questions about product lifecycle, development, and creativity:
- How does your organization encourage innovation and thinking outside the box?
- Outline a process at your organization that is well defined. Now describe something that is not well-defined. How were these examples successful and unsuccessful?
What are the shape of their products? It is not unheard of for organizations to over embellish about their products and services. Prototypes only go so far and at some point ask to view and play with the latest, most stable product.
- How has the [[product]] evolved since the first iteration?
- Please discuss the various releases of [[product]] and changes in each iteration?
- What are the future plans for this product? Who will be implementing this plan?
- If you’re talking to a start-up then the funding question is important too.
What dedicated resources and team members to data science?
- Survey the organization’s dedicate to analytics, technical feedback, financials, and data gathering. It’s all about “data science” because likely they will not have labeled it that way in the past.
- Overall freedom to think, innovation, and connection. Do you have adequate time to code/heads down work AND communicate with people?
Other factors of workplace culture:
- Pace – How face will you be expected to churn out insights and reports?
- Office Space Look and Feel – Cleanliness is necessary for Happiness? Are you inspired?
- What is the space energy? Will it replenish or deplete you every day?
- How do they plan on integrating quantitative insights? How is this plan implemented?
Photo Credit: Startup Stock Photos