Edit

Project roles

Goal: Give insight into the people/roles engaged in every stage of Use Case implementation, as well as their main responsibilities.

Every Use Case, either Information Extraction or Classification, POC/Pilot or Production, implies involvement of a team of: Solutions Consultants, Business Analysts, Account Managers, Delivery Managers and more.

Let's define roles for those who work closely with the Data Analyst: Delivery Manager, ML Engineer, Automation Engineer Specialist, Data Scientist, and Data Analyst.

Role	Responsibilities
Delivery Manager (DM)	Coordinates the project during its whole lifecycle: Validate business process design: data flows, rules, RPA and ML steps. Define which models will be needed to train. Consult a customer about WorkFusion products and offerings. Justify a solution to the customer and explain required resources, for example, explain statistics, reasons, methods, resources needed to improve. Communicate and explain key risks to the customer. Prepare project planning of the WorkFusion implementation. Estimate each phase of the implementation project: tag data, prepare data set, train model, develop bots, test whole process. Plan project team capacity: number of SMEs, DA, MLE and other roles. Staff a project team: conduct interviews, ask the right questions and make decisions (with the help of experts in areas where needed). Present the project plan to management and gain approval. Perform continuous monitoring of a project status. Over time, identify and eliminate the risks arising in the project. Quickly find solutions for project-related issues like: installation issues, improving data quality, improving OCR quality, deployment issues, and so on.
ML Engineer (MLE)	Launch training and tests of IE or Classification models (along with DA). Configure ML environment, makes sure that ML-related components are up and running. Help DA get ML statistics and model results analysis. Define post-processing logic in cooperation with DA and implement it. Fix OCR errors, to achieve the best P/R scores (compliant with customer's requirements). Create custom models using AutoML SDK.
Automation Engineer Specialist (AES)	Develop RPA part of the business process by creating software robots. Launch training and tests of IE or Classification models (along with DA) using out-of-the-box models.
Data Scientist (DS)	Perform the deepest model customization. Handle most complex cases. Create non-standard additional model components, if necessary.
Data Analyst (DA)	Build data models and reporting packages. Analyze large data sets. Write comprehensive reports. Develop and implement a problem-solving approach.