Automation, Data Science, and PMO Lead

Job Description

As the Automation, Data Science and PMO Lead, you will play a pivotal role in driving automation initiatives and leveraging data science techniques to enhance operational efficiency, drive insights, and support strategic decision-making. You will work closely with cross-functional teams to identify automation opportunities, develop advanced analytics solutions, implement data-driven strategies and project manage digital innovation and transformation initiatives that drive business growth and innovation.

Metrics

• Automation Rate: Percentage of processes automated compared to total processes identified for automation.
• Cost Savings: Quantifiable cost savings achieved through automation initiatives, including reductions in labor costs, error rates, and processing times.
• Process Efficiency: Improvement in process efficiency metrics, such as cycle time reduction, throughput, and resource utilization, as a result of automation efforts.
• Data Accuracy: Accuracy and reliability of data maintained within automated systems and data science models, measured by error rates and data quality metrics.
• Insights Generated: Number of actionable insights and recommendations generated from data analysis and predictive modeling efforts, leading to improved decision-making.
• ROI of Automation Projects: Return on investment achieved through automation projects, calculated based on cost savings, productivity gains, and business impact.
• Model Performance: Performance metrics of data science models, including accuracy, precision, recall, and F1 score, evaluated through validation and testing.
• Customer Satisfaction: Feedback and satisfaction ratings from internal stakeholders and end-users regarding the effectiveness and usability of automation solutions.
• Training Effectiveness: Assessment of the effectiveness of training programs and knowledge sharing initiatives, measured by employee proficiency and adoption of automation and data science techniques.
• Time-to-Value: Speed of delivering automation solutions and generating actionable insights from data, from ideation to implementation and realization of business value.
• Project success rate: Measure the percentage of digital innovation projects that are completed on time, within budget, and meeting predefined success criteria.

Responsibilities (1 of 2)

• Automation Strategy: Develop and execute the automation strategy, identifying areas for process automation and optimization across the organization.
• Data Analysis: Utilize advanced analytics techniques to extract insights from data, identify trends, and support data-driven decision-making.
• Automation Development: Design, develop, and implement automation solutions using tools such as robotic process automation (RPA), scripting languages, and workflow automation platforms.
• Data Modeling: Build and deploy predictive models, machine learning algorithms, and statistical analyses to solve business problems and improve operational efficiency.
• Process Improvement: Identify opportunities for process improvement and optimization through automation and data-driven approaches, collaborating with stakeholders to implement solutions.
• Tool Evaluation: Research and evaluate emerging automation and data science tools and technologies, recommending solutions that align with business objectives and requirements.
• Cross-functional Collaboration: Collaborate with business units and functional teams to understand their automation and data science needs, provide guidance, and deliver solutions that meet their requirements.
• Quality Assurance: Ensure the accuracy, reliability, and integrity of data and automation solutions through rigorous testing, validation, and quality assurance processes.
• Training and Knowledge Sharing: Provide training, mentorship, and knowledge sharing sessions to colleagues on automation tools, data science techniques, and best practices.
• Continuous Improvement: Stay abreast of industry trends, best practices, and emerging technologies in automation and data science, continuously improving skills and capabilities to drive innovation and excellence.
• Project governance, planning and execution: leading PMO for digital innovation projects from ideation to implementation as aligned with business objectives.

Responsibilities (2 of 2)

• Familiarity with data visualization tools such as Tableau, Power BI, or matplotlib, for presenting insights and findings to stakeholders.
• Excellent problem-solving and analytical skills, with the ability to translate complex business requirements into practical automation and data science solutions.
• Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders at all levels of the organization.
• Detail-oriented with a focus on quality and accuracy, ensuring data integrity and reliability in all automation and data science initiatives.
• Continuous learner with a passion for staying updated on industry trends, best practices, and emerging technologies in automation and data science.
• Proven track record of successfully leading complex projects from inception to completion, delivering business value and driving innovation.
• Preferably with knowledge in Lean Six Sigma, Design Thinking and Operational Maturity methodologies.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, Business or related field; advanced degree preferred.
• At least 5 years of experience in automation, data science, or related roles, with a focus on developing and implementing automation solutions and data-driven insights.
• Minimum of 5 years of experience in project management, with focus on digital innovation and transformation initiatives.
• Proficiency in programming languages such as Python, R, or Java, with experience in scripting, data manipulation, and automation scripting.
• Strong understanding of automation technologies and frameworks, including robotic process automation (RPA), workflow automation platforms, process mining tools and dashboard (Power Apps, Power BI etc.)
• Expertise in data analysis, statistical modeling, and machine learning techniques, with hands-on experience in building and deploying predictive models and algorithms.

Benefits

• Guaranteed 14th month pay
• Above-market Retirement Plan design
• LinkedIn Learning access
• Established Performance Incentive Program
• HMO coverage for employees on day 1
• Free HMO coverage for up to 3 qualified dependents
• Educational assistance