Data Science & Performance Analytics Lead 1

About The Role

The Data Science & Performance Analytics Lead is accountable for transforming IT service data into actionable insights, predictive intelligence, and performance visibility to drive better decision-making across the IT service landscape.

This Role Establishes a Data-driven Performance Management Capability, Enabling

• Clear visibility of service performance and experience
• Proactive identification of risks, trends, and inefficiencies
• Continuous improvement through analytics and data science techniques
  
  

Operating across a multi-vendor environment, the role ensures consistent, trusted, and meaningful use of data to improve service stability, user experience, and operational efficiency.

The position shifts the organization from reactive reporting to predictive, insight-led service management.

At International SOS, our mission is to safeguard the global workforce against health and security risks while delivering specialized medical services to our clients. Headquartered in Singapore, we are a high-growth, fast-paced organization. Our culture thrives on entrepreneurship, fuelled by a strong sense of purpose: saving lives and protecting people every day. We empower our staff, fostering an energetic and challenging work environment that encourages individual growth. As part of our global outlook, you'll collaborate with multi-disciplinary, multi-cultural teams to tackle significant challenges. We welcome self-driven individuals who aspire to make a meaningful impact.

If you're passionate about technology, and operational excellence, and driving impactful change, we invite you to apply for this exciting role!

Job Responsibilities

Analytics & Dashboards

• Own Framework: Set up and manage KPIs, SLAs, and experience metrics (XLAs).
• Build Dashboards: Track incident, change, service uptime, and CSAT in one place.
• Guide Leaders: Give IT and business stakeholders clear, useful insights.
  
  

Insights & Predictive Data

• Spot Trends: Read operational data to find root causes and early risk signs.
• Forecast Demand: Use predictive models and AI to guess ticket volumes and outages.
• Flag Risks: Catch high-risk changes before they break services.
  
  

Integration & Quality

• Unify Data: Build a single source of truth across all ITSM teams.
• Govern Quality: Work with CMDB teams to keep reporting data clean and consistent.
  
  

Vendor & Automation

• Track Vendors: Score suppliers on SLAs, service quality, and contract targets.
• Automate Reports: Move to auto-reporting using ServiceNow, Power BI, and AIOps tools.
• Drive Improvement: Use trend benchmarks to cut waste and build a data-first culture.
  
  

Job Requirements

Required Work Experience

• 8–12 Years: Enterprise data analytics, data science, or IT performance.
• Leadership: Led IT and operational reporting capabilities.
• ITSM Data: Deep experience with Incident, Request, Change, and Problem data.
• Advanced Methods: Used trend analysis, forecasting, and risk modeling.
• Exec Dashboards: Built KPIs using Power BI, Tableau, and ServiceNow.
• Vendor Governance: Tracked third-party SLAs and performance metrics.
• Data Strategy: Unified cross-functional data into a single source of truth.
  
  

Required Qualification

• Bachelor's Degree in Data Science, Computer Science, Information Systems, or related discipline.
• Certifications in Data Science / AI / Machine Learning (e.g., Python, ML, AI fundamentals) – desirable
• ITIL Foundation – desirable, with understanding of ITSM data and processes
• Certifications or training in Data Governance, Data Management, or Data Quality frameworks – advantageous
• Strong foundation in statistics, data modelling, and analytical methods
  
  

Skills Required

Required Skills and Knowledge

• Strong expertise in data analytics, reporting, and performance management.
• Understanding of IT Service Management (ITIL processes) and operational metrics.
• Experience with data visualisation tools (e.g., Power BI, Tableau, ServiceNow reporting).
• Knowledge of data science techniques (e.g., forecasting, trend analysis, ML basics).
• Strong analytical and problem-solving skills with ability to translate data into insights.
• Understanding of multi-vendor environments and service performance metrics.
• Strong communication skills to present insights to technical and business stakeholders.
• Strong communication and stakeholder management skills.