This document outlines roles and processes for building an effective data science team. It recommends that teams include people with skills in machine learning, data engineering, analytics, mathematics, and visualization. The document describes the CRISP-DM process for data science projects and a lambda architecture for continuous analysis of big data using a pipeline of jobs, messaging queues, and data processing applications. It emphasizes building cross-disciplinary teams that can work together to ask important questions and create useful data products.

1. Data Science Team
A practice to set up
Omid Mogharian
V0.2.1 - 06.02.2017

2. … it’s a mistake to treat data science teams like any old product group ... To build
teams that create great data products, you have to find people with the skills and
the curiosity to ask the big questions. You have build cross-disciplinary groups with
people who are comfortable creating together…
DJ Patil, U.S. Chief Data Scientist at White House
Office of Science and Technology Policy

3. Roles
Machine Learning
Engineer
Data
Engineer/Architect
Data Analyst
Math &
Statistics
Interpretation/
Visualisation
Modeling &
ML
Math &
Statistics
Machine
Learning
Developing
Developing
Infrastructure
Design
Operation
Data Scientist
Core Team Skills

4. Relations
Data Science Team
Operations/
System
Administration
Sales
PO/
Customer
relation
BI
App
Development
To make it real

5. Method
CRISP-DM
The Data Science Process
Communications with Customer

6. How to? lambdaBig data and Fast data

7. Big Data Pipeline
Job repository
Scheduler/
Runner
Incremental
Runner
Message QueueData Pipe Agent
A practice for continuous analyse
Severing
layer

8. Application Environments
Source
Simulator
Stage
Production
Sample Data
Source
Connector
Big Data
Continuous Analyse Application*
Continuous Analyse Application*
To bring accuracy
* Whole software with
several components
which are explained in
previous slide

9. References
● https://dzone.com/articles/lambda-architecture-with-apache-spark
● https://en.wikipedia.org/wiki/Lambda_architecture
● https://www.mapr.com/developercentral/lambda-architecture
● http://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html
● http://www.kdnuggets.com/2016/03/data-science-process-rediscovered.html
● http://www.datascienceassn.org/sites/default/files/Building%20Data%20Scienc
e%20Teams.pdf