Machine Learning Team Roles
Machine Learning Team Roles
ML project team usually involves such specialists as Data Analyst, Data Scientist, ML Engineer and Subject Matter Expert(s) from customer side.
Data Analyst (DA)
In data set collection, the Data Analyst studies business logic of the Use Case and applicable documents, and defines the rules and corner cases. The Data Analyst works closely with the Subject Matter Experts (SMEs) from the customer/partner side on the Data Set collection step. The Data Analyst trains SMEs to tag the documents and verifies tagging.
Later (after model training) the Data Analyst analyzes ML results and defines rules that help to improve the model. ML results are evaluated by comparing the results provided by people (manual extraction or classification that is considered to be correct) and calculating quality metrics statistics. The Data Analyst calculates interim and final statistics and submits these to the customer in the form of a report.
At the analysis of results stage, the Data Analyst needs to review the model execution results and, if necessary, propose ways to improve. The model is trained on the test set for each training iteration. If there are several iterations, the Data Analyst calculates statistics of each iteration based on evaluation results. The Data Analyst needs to calculate statistics for each iteration and analyzes the delta for tagging iterations and model mistakes.
During the final report, analytics helps DAs and business users to see and understand outcomes of automation in production. This may include aggregated numbers showing savings, amount of work done, SLAs, average ML measures, etc.
Data Scientist (DS)
ML improvements and retraining is handled by the Machine Learning Engineer or Machine Learning Engineer + Data Scientist. This is the most complex stage, as it includes creation of custom model components.
ML Engineer (MLE)
The Machine Learning Engineer or Automation Engineer Specialist leads model training. Please, refer to the following links for more information: Train ML model (Classification), Train ML model (IE). MLE also improves and retrains the model when necessary.
Subject Matter Expert (SME)
Subject Matter Experts (SMEs) have a deep understanding of documents' logic and tag the data set for model training. They help DA understand business logic of the Use Case and tag the documents after completing the necessary training.
The following image illustrates the stages of ML project and the involvement of team members:
