Many companies face the challenge of building up a data science team from scratch and it can be hard to figure out how to start. In 2016, I was the first hire of a new data science team, with little infrastructure or strategy in place. Over the years, there were many different challenges for us to solve and mistakes to learn from as the team got more and more mature. This talk is about what I learned about the process of building up a data science team, from both my own experience in the past years and conversations with other data scientists in a similar situation.

1. Building up a
Data Science Team
Amadeus Magrabi
Lead Data Scientist, commercetools

2. Plan for today
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• Background:
o Me
o commercetools
o Data Science @ commercetools
• What I learned about building up a data science team
o Hiring
o Communication
o Choosing projects
o Choosing tools
• Questions

3. Background
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Me:
• Studied cognitive science
• Research in neuroscience/ML for 3 years
• Working in data science since 3 years
commercetools:
• Offers e-commerce software via cloud-based APIs
• Founded in 2006
• 150+ employees
• Main offices in Berlin, Munich, Durham (US)

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Data Science @ commercetools:
Image Similarity Search
Search Image Prediction 1 Prediction 2 Prediction 3

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Data Science @ commercetools:
Category Recommendations

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Data Science @ commercetools
Team structure:
• 3 data scientists
• 2 software engineers
• 1 product owner
• 1 working student
Team output:
• For merchants:
o APIs that make it easier to manage data and improve data quality.
• For customers:
o APIs that enable innovative data-based features.
• For colleagues:
o Make internal company processes more data-driven,
more efficient and more accurate.

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Lessons learned from building up
the data science team at
commercetools

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Lesson 1:
Job titles in data science are vague and unreliable.
• Data Scientist:
Vague combination of knowledge about statistics, machine learning,
software engineering and business domains.
• Machine Learning Engineer:
Focus on building ML-based software products.
• Data Analyst:
Focus on statistics, visualizations and business insights.
• Data Engineer:
Manages ETL pipelines to make data accessible and maintain data quality.
• Software Engineer:
Ensures code is efficient, scalable and maintainable.
• Machine Learning Researcher:
Focus on publishing research papers.
• Machine Learning Product Owner:
Communicates with stakeholders to define requirements and priorities.
Software
Engineering
Statistics/
Machine
Learning
Business
Knowledge
Data
Science

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Lesson 2:
Do not try to find someone who can do everything.
Build a balanced and autonomous team instead.
Software
Engineering
Statistics/
Machine
Learning
Business
Knowledge
Data
Science
generalist
specialist
unicorn

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Lesson 3:
Prioritize simplicity over business impact for the first projects.
vs.

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Lesson 4:
Talk to everyone and set the right expectations.
• Talk to people across the whole organization
to identify the most valuable data projects.
• Find data enthusiasts that support your initiatives
and convince skeptics.
• Over-communication is better than under-communication.
• Avoid setting overly optimistic expectations
(better to under-promise and over-deliver).

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Lesson 5:
Find a good balance between flexibility and consistency of tools.
Flexibility makes
prototyping and
hiring easier.
Consistency makes
collaboration and
productionizing
easier.

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Lesson 6:
Data science is a long-term investment.
rare
more common

14. Thanks for listening!
Summary
1. Job titles in data science are vague and unreliable.
2. Build a balanced and autonomous team.
3. Prioritize simplicity over business impact for the first projects.
4. Talk to everyone and set the right expectations.
5. Find a good balance between flexibility and consistency of tools.
6. Data science is a long-term investment.
Social media
twitter.com/AmadeusMagrabi
linkedin.com/in/amadeusmagrabi
medium.com/@amadeus.magrabi