Solution Design |
- Business/Tech Requirement assessment
- Validate business process design - data flows, rules, RPA & ML steps.
- Define which models will be needed to train.
- Describe solution design to the customer.
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- Knowledge of all major releases of the Intelligent Automation Cloud, key components and its purpose
- SOW requirements and success criteria review and acknowledgement
- Knowledge of RPA and ML use-cases: how to automate typical processes in key industries (banking, finance services, healthcare, insurance, retail)
- Knowledge of data types and its usage in WorkFusion SPA
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| Customer Education |
- Explain the basics of ML to customer and respond on typical questions.
- Consult customer about WorkFusion products and offering.
- Justify a solution to the customer and required resources, for example: explain the statistics, reasons, ways, needed resources to improve.
- Communicate and explain to the customer major implementation risks
- Coordination of Installation and Upgrade activities
- Cover basic WF architecture-related questions
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- Knowledge of current WorkFusion offering, current and planned versions of products and its capabilities.
- Knowledge of the key risks of WF Intelligent Automation Cloud implementations and ways how to mitigate it in typical projects environments.
- Ability to prepare and communicate plans, proposals, estimations by the project and present it to the customer.
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| Project Management |
- Prepare project planning of WorkFusion implementation.
- Estimate each phase of implementation project - data tagging, preparing data set, train model, develop bots, testing whole process.
- Optimize plan to meet customer expectations.
- Plan the project team capacity - the number of SMEs, DA, MLE and other roles.
- Staff a project team - to conduct an interview, ask the right questions and make a decision (with help of Exerts in areas where needed).
- Present the project plan to the management and approve it.
- Continuous monitoring of project status.
- In 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 etc.
- Motivate team members to achieve results.
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- Knowledge of RPA and ML projects life cycle, typical phases and tasks for each type of implementation.
- Knowledge of which tasks can be performed in parallel.
- Knowledge of typical estimations for key project phases: data tagging, preparing data set, train model, develop bots.
- Ability to prepare and conduct project kick-off meeting.
- Ability to prepare weekly RAG status by the project.
- Proper business-communication skills (emails, face-2-face, onsite visits, ).
- Knowledge of all support options a DM has (Forum, documentation, Internal and External CoEs, Product Service Desk).
- Knowledge of best practices of Intelligent Automation Cloud development: OCR, RPA, data tagging.
- Ability to calculate team capacity for the product relates to amount of expected data set and use case complexity.
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