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