Predictive Analytics Lead

We are seeking a Predictive Analytics Lead to turn existing capital project datasets into forward-looking, predictive insights that improve decision-making across a portfolio of data centre construction projects. This is a data-first role, focused on analysing historical and live project data, identifying patterns and trends, and developing predictive and forecasting models that anticipate delivery risks, performance issues, and outcomes before they occur. The role will require a strong data science or advanced analytics professional who can apply predictive techniques to complex, real-world datasets and translate results into clear, actionable insights for senior stakeholders., Predictive Analytics & Modelling

• Analyse large, complex datasets already gathered across capital project and programme environments.
• Develop predictive models to forecast project risks, delivery delays, cost escalation, and performance outcomes.
• Build scenario and "what-if" models to assess the impact of different variables and interventions.
• Design and test leading indicators and early-warning signals based on data patterns and correlations.
Insight Generation & Decision Support
• Identify recurring trends and patterns within project data that indicate future risk or opportunity.
• Translate analytical outputs into explainable, decision-ready insights for non-technical stakeholders.
• Present outcomes with clarity, including confidence levels, assumptions, and limitations.
• Support leadership teams with predictive insights that enable early intervention and risk mitigation.
Data Strategy & Analytics Delivery
• Shape how predictive analytics capability is applied within capital projects and programme environments.
• Advise on data quality, structure, and modelling approaches to maximise predictive value.
• Collaborate with cross-functional teams (programme, delivery, digital, leadership) to ensure analytics outputs are embedded into decision-making processes.
• Contribute to the development of analytics products such as dashboards, reports, and forecasting tools.
Continuous Improvement
• Evaluate model performance over time and refine approaches as new data becomes available.
• Promote best practice in data modelling, analytics, and evidence-based decision-making.
• Document models, methodologies, and insights to ensure transparency and repeatability., Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability. If you are a qualified candidate and need assistance or an accommodation, please let us know by completing .
  
  Strong experience in predictive analytics, statistical modelling, or data science.
• Hands-on capability with Python or R (e.g. pandas, scikit-learn, regression, classification, time-series analysis).
• Proven experience analysing large, messy, real-world datasets.
• Strong understanding of forecasting, probability modelling, and model validation.
• Ability to clearly explain complex analytical models to non-technical audiences.
• Experience translating data insights into practical, business-focused recommendations.
Desirable
• Experience applying analytics in complex operational, infrastructure, supply-chain, or programme environments.
• Familiarity with Power BI, Tableau, or similar tools for data visualisation and storytelling.
• Background in consulting, advisory, or insight-driven roles working with senior stakeholders
  
  Equinix is the world's digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.
A place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future. A career at Equinix means being at the center of shaping what comes next and amplifying customer value through innovation and impact. You'll work across teams, influence key decisions, and help shape the path forward. You'll find belonging, purpose, and a team that welcomes you-because when you feel valued, you're empowered to do your best work.