.Data Scientist – AI and Automation in Customer Success Full-time Company Description We are in Business for People, empowering people in service organizations with innovative Enterprise and Business software solutions.
We've innovated and taken a new approach to delivering ERP that works for people.
Self-driving, adaptive, and intuitive software that is changing the way people work.
Our solutions empower people and deliver a better people experience so people can spend time on meaningful high-value work they live for.
Job Description We are looking for an experienced and highly motivated Data Scientist specializing in Artificial Intelligence and Machine Learning to drive the automation of business processes across the LAER lifecycle.
This hands-on role will focus on leveraging data science techniques to reduce manual tasks, streamline workflows, and integrate data across systems to optimize customer outcomes.
You will work closely with cross-functional teams to create, implement, and monitor automation solutions that directly enhance customer experience and operational efficiency.
Key Responsibilities Data Integration & Automation: Develop and deploy machine learning models to automate processes and integrate customer data across multiple systems of record.
Process Optimization: Analyze and improve customer workflows, identifying opportunities for automation to remove manual effort within the LAER lifecycle stages.
Predictive & Prescriptive Analytics: Build predictive models to anticipate customer needs, enabling proactive support and decision-making within the Customer Success team.
Tool Development: Create tools and frameworks to enable Customer Success Managers (CSMs) to interact with automated insights, reducing repetitive tasks and enhancing customer interaction efficiency.
Collaborative Solution Design: Work closely with the Product, Data Engineering, and Customer Success teams to ensure that AI solutions are well-aligned with customer outcomes and business objectives.
Continuous Improvement: Regularly evaluate the performance of deployed models and adjust them to ensure they meet evolving customer and business needs.
Key Accountabilities Process Automation Implementation: Design, test, and deploy machine learning models and automation solutions.
Performance Monitoring & Adjustments: Track the success of automation tools and adjust algorithms to optimize their effectiveness and align with customer outcomes.
Data Accuracy & Integration: Ensure data consistency across systems and work to enhance data integration for a seamless end-user experience.
Stakeholder Communication: Regularly report on automation initiatives and impact metrics to stakeholders, demonstrating value and return on investment.
Key Metrics for Success Automation Coverage: Percentage of LAER processes automated with minimal manual intervention required