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What is the A.I. Avenger Team – 7 Avenger Types
A.I. Product visionary
• Business-customer-product context
• Nature of available data - inside and outside
• ‘Art of the possible’ with A.I.
• Building end-to-end models
• Implementation planning
Statistician or Analyst
• Getting hands dirty with the data
• Patterns in data, clues for design
• Model prototypes
• Production ready models
Model-builder
• Building full models
• Model evaluation
• Model debugging
• Data analysis (often but not ideal)
• Product context (again not ideal)
The Implementer
• Take models into production
• How to get a model to scale in production
• Building with existing model services/APIs
• Why a model is failing
• How to improve the modelling process
Big Data Engineer
• Prepare big data sets, ready for models
• Understand problem to access relevant
signals and build a strong dataset
• Data hygiene
• Model building
• Data analysis
The Data Story-teller or “Translator”
• Communication with product and business on
how and why a model is working
• Finding opportunities to improve models with
problem intuition and system understanding
• Model building
• Model implementation
The Full Workflow Data Scientist
• Sees end-to-end picture on what it
takes to make a model successful
• Capable at every step of the workflow
• Silo role
• Limited scope
Product visionary or opportunity finder
0-1
What roles when?
1-10
10-100
Statistician or Analyst
The Implementer (using existing services)
Big Data Engineer
Model-builder
The Translator
More Implementers
Full Workflow Data Scientists
+
+
Authors
Avi Patchava
@avipatch
Founders of Bright Capital
Varun Modi
@VarunKumarModi2

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The A.I. Avenger Team - The 7 roles that you need

  • 1. What is the A.I. Avenger Team – 7 Avenger Types
  • 2. A.I. Product visionary • Business-customer-product context • Nature of available data - inside and outside • ‘Art of the possible’ with A.I. • Building end-to-end models • Implementation planning
  • 3. Statistician or Analyst • Getting hands dirty with the data • Patterns in data, clues for design • Model prototypes • Production ready models
  • 4. Model-builder • Building full models • Model evaluation • Model debugging • Data analysis (often but not ideal) • Product context (again not ideal)
  • 5. The Implementer • Take models into production • How to get a model to scale in production • Building with existing model services/APIs • Why a model is failing • How to improve the modelling process
  • 6. Big Data Engineer • Prepare big data sets, ready for models • Understand problem to access relevant signals and build a strong dataset • Data hygiene • Model building • Data analysis
  • 7. The Data Story-teller or “Translator” • Communication with product and business on how and why a model is working • Finding opportunities to improve models with problem intuition and system understanding • Model building • Model implementation
  • 8. The Full Workflow Data Scientist • Sees end-to-end picture on what it takes to make a model successful • Capable at every step of the workflow • Silo role • Limited scope
  • 9. Product visionary or opportunity finder 0-1 What roles when? 1-10 10-100 Statistician or Analyst The Implementer (using existing services) Big Data Engineer Model-builder The Translator More Implementers Full Workflow Data Scientists + +
  • 10. Authors Avi Patchava @avipatch Founders of Bright Capital Varun Modi @VarunKumarModi2