Recruiting for Data Science

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 When building a data science team, companies often face the challenge of finding individuals with a comprehensive skill set, including domain-specific knowledge, proficiency in analyzing both structured and unstructured data, and excellent presentation and storytelling skills. This desire for an all-encompassing skill set may lead to the pursuit of a "unicorn" candidate, someone who possesses a rare combination of skills. However, the video suggests that the odds of finding such a candidate are low.

Instead of solely focusing on technical skills, the video recommends considering factors like curiosity, a sense of humor, and passion for the specific business domain when hiring for a data science team. Curiosity is essential as it drives individuals to explore various aspects of their environment, including data. A sense of humor is valued to maintain a lighthearted approach, ensuring that individuals don't take themselves too seriously. Passion for the business domain is crucial, as it determines how effectively a data scientist can contribute to the organization's productivity.

The hiring process should prioritize social skills, curiosity, storytelling ability, and a sense of humor over technical skills. While technical skills are important, they can be taught, whereas curiosity, storytelling, and a sense of humor are intrinsic qualities that contribute to a candidate's overall fit with the organization.

The video also highlights the importance of understanding the role a candidate will play in the data science team. Whether the organization needs engineers, architects, designers for visualization, or individuals with expertise in handling large matrices, the hiring process should align with the team's objectives.

From a technical skills perspective, the video suggests considering the specific technical platform and domain in which the candidate will work. The choice between structured data environments, big data environments, or traditional predictive analytics environments determines the relevant tools and skills needed. Platforms like R, Stata, Python, Hadoop, and Spark are mentioned, each suited to different types of data and analyses.

Communication skills, including the ability to tell a compelling story and present findings effectively, are emphasized as equally important as technical skills. The video compares presenting findings to a journey where unexpected turns reveal awe-inspiring views, highlighting the joy and empowerment that effective communication can bring to clients.

In summary, the hiring process for a data science team should prioritize qualities such as curiosity, a sense of humor, and passion, in addition to technical skills. Understanding the specific role within the team and aligning technical skills with the chosen platform and domain are crucial for building a successful and effective data science team.

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