What is the most effective way to structure a data science team?

What is the most effective way to structure a data science team?

4/8/2018

link

https://towardsdatascience.com/what-is-the-most-effective-way-to-structure-a-data-science-team-498041b88dae

summary

This blog post discusses the ideal structure for a data science team within an organization. It explores the different roles and positions that should be included in a data science team, such as data engineers, data analysts, and machine learning engineers. The article also highlights the importance of having clear goals and objectives for the team, as well as establishing effective communication and collaboration channels. It provides insights into the different team structures commonly used in the industry, such as centralized, decentralized, and hybrid models, and discusses the pros and cons of each. The author emphasizes the need for cross-functional collaboration and suggests that the most effective data science teams are those that have a balance of technical expertise, domain knowledge, and business acumen.

tags

data science ꞏ data analysis ꞏ data team ꞏ team structure ꞏ effective team ꞏ data-driven decision making ꞏ data strategy ꞏ team collaboration ꞏ team roles ꞏ data projects ꞏ project management ꞏ data engineering ꞏ data visualization ꞏ machine learning ꞏ data scientists ꞏ data managers ꞏ data governance ꞏ data infrastructure ꞏ data skills ꞏ data culture ꞏ data-driven organization ꞏ team communication ꞏ agile methodology ꞏ data ethics ꞏ data quality ꞏ team leadership ꞏ team efficiency ꞏ data analytics ꞏ data architecture ꞏ data modeling ꞏ data integration ꞏ data literacy ꞏ data tools ꞏ data technology ꞏ data resources ꞏ data pipelines ꞏ team productivity ꞏ team performance ꞏ team dynamics ꞏ data-driven insights ꞏ data-driven solutions ꞏ team empowerment ꞏ team motivation ꞏ team success ꞏ team growth ꞏ team development ꞏ team training ꞏ team diversity ꞏ data team structure