For me, a data scientist is the adult version of the kid who can’t stop asking “Why?”.  I am the kind of person who goes into an ice cream shop and gets two different scoops on their cone because they really need to know what each one tastes like.  Of course, what someone whose job title is data scientist will do at a given company depends on the company and the person, and may look more like one of those other titles, rather than a mixture of all three.

From my experience, a Data Scientist is someone who makes value out of data. Such a person proactively fetches information from various sources and analyzes it for better understanding about how the business performs, and to build AI tools that automate certain processes within the company.

The order of these tasks is intentional, and it roughly reflects the life cycle of a data science project. To be fair, we should add “0. Data cleaning” to that list, as it can be one of the most time consuming tasks of a data scientist. It’s also an incredible litmus test for data scientists. Someone who can’t parse a messy CSV isn’t going to cut it as a data scientist). Let’s look at these tasks in more detail. 

Why Assistants Director expecialist in Data Scientists , like me, are different?

Successfully assistant in data science team requires skills and philosophies that are different from those that arise in managing other groups of smart professionals. It’s wise to be aware of the potential organizational frictions and trade-offs that can crop up.

While businesses are hiring Assistant Secretary specialized in Data Scientist, many struggle to realize the full organizational and financial benefits from investing in data analytics. This is making managers to hire Assistant Secretary specialized in Data Science.

How can organizations realize the promise of the evolving disciplines that we broadly call analytics?

Organizations requires skills and philosophies that are different from those that arise in managing other groups of smart professionals.

Rather than just involving oversight and planning, managing a data science research effort tends to be a dynamic and self-correcting process; it is difficult to plan precisely either a project’s timing or final outcomes. For those unused to this type of work, this process can seem quite messy — an unexpected contrast to a field that, from the outside, seems to epitomize the rule of reason and the preeminence of data.

Compounding the friction that this uncertainty generates is the highly technical nature of quantitative research, which can strain relationships between data science teams and other business units. In most organizations, the consumers of data mining or analytic modeling are line Assistant Secretary specialized in Data Science.


Izaskun Larrea Manzarbeitia

Data Scientist & Assistant Director

To read more about Big Data and Business Intelligence

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