Service Data Analytics Specialist at Nexio

ROLE PURPOSE


The Service Data Analytics Specialist is accountable for building the analytical and technical capability that turns the SDM's operational data into predictive, diagnostic insight - going beyond reconciliation and status reporting into trend modelling, anomaly detection, and forward-looking risk signalling. This role exists to shift the department from reactive reporting ("what happened") to predictive positioning ("what's about to happen and why it matters") - giving the SDM early warning on service degradation, capacity strain, or client risk


What This Role Owns (Outcome Commitments):


Predictive & Diagnostic Analytics - Trend, pattern, and anomaly analysis across service performance data (incidents, SLA/SLO performance, capacity, XLA/experience metrics) that surfaces risk before it becomes visible operationally.
Analytics Tooling & Dashboard Infrastructure - Design, build, and maintenance of dashboards, automated reporting pipelines, and analytical tooling that reduce manual reporting effort and increase reporting frequency/accuracy.
Statistical Rigour - Application of sound statistical and analytical method (not spreadsheet-level approximation) to service data - ensuring conclusions are defensible, not directional guesses dressed as insight.
Early-Warning Risk Signalling - Proactive identification of deteriorating trends (SLA drift, capacity strain, recurring incident patterns) communicated to the SDM in time to act, not after the fact.
Data Model & Source Integrity for Analytics - Ensuring the underlying data models and pipelines feeding analytics are structurally sound, distinct from the BA's day-to-day data reconciliation for reporting purposes.


ROLE ACCOUNTABILITIES / KEY ACTIVITIES

Predictive & Diagnostic Analytics
Accountable for: Surfacing forward-looking risk from service data before it becomes an operational or client-facing issue.


Build and maintain trend analysis across incident volumes, SLA performance, and capacity data to detect early signs of service degradation
Apply statistical methods (e.g. variance analysis, forecasting, correlation analysis) to distinguish genuine risk signals from normal operational noise
Conduct pattern analysis on recurring incidents to identify systemic risk that individual RCAs may miss in isolation
Deliver early-warning signals to the SDM with sufficient lead time to act - not retrospective confirmation of what already happened


Analytics Tooling & Dashboard Infrastructure
Accountable for: The technical infrastructure that makes predictive and diagnostic analysis possible and repeatable.


Design, build, and maintain automated dashboards for SLA performance, capacity trends, and XLA/experience metrics across the SDM's portfolio
Develop and maintain data pipelines that reduce manual data pulling and consolidation effort, increasing reporting frequency and accuracy
Ensure dashboard and tooling outputs are structured for the SDM's actual decision-making needs — not generic BI output disconnected from operational reality
Maintain version control and documentation on analytical models and tooling logic, so methodology is auditable and repeatable.


Statistical & Methodological Rigour
Accountable for: Ensuring analytical conclusions are defensible under technical scrutiny, not directional approximation.


Apply appropriate statistical technique to each analytical question - avoid overfitting simple trend lines to complex operational reality
Validate data quality and sample sufficiency before drawing conclusions; flag where data volume or quality is insufficient for a reliable signal
Distinguish correlation from causation explicitly in any diagnostic output - never imply causal relationships the data doesn't support


XLA & Experience Data Integration

Accountable for: Extending analytics beyond traditional SLA metrics into end-user and digital experience data, in line with the department's XLA maturity direction.


Integrate digital experience monitoring and sentiment data sources into the analytics view where available for the SDM's accounts
Build the analytical bridge between traditional operational metrics (MTTR, availability) and experience-based metrics (end-user satisfaction, sentiment trends)
Support the department's shift toward XLA-informed reporting by piloting experience-data analysis on suitable accounts
Flag where XLA data collection or tooling gaps limit the department's ability to report on experience-level trends


Data Model & Source Integrity for Analytics
Accountable for: The structural soundness of the data models and pipelines underpinning analytics


Define and maintain data models that support analytical use cases (trend analysis, forecasting) rather than only point-in-time reporting
Coordinate with the Business Analyst to ensure analytics-layer data models remain aligned with reconciled source-of-truth data, avoiding two competing versions of the same metric
Identify structural data gaps that limit analytical capability (e.g. missing historical data, inconsistent tagging/categorisation) and drive resolution
Ensure analytical tooling and models comply with data handling, particularly where experience/sentiment data involves personal information


Advisory Input to the SDM
Accountable for: Translating analytical findings into decision-relevant input for the SDM, not raw technical output.


Present analytical findings in business-impact terms - what the trend means operationally and commercially, not just the statistical result
Support the SDM's client service reviews and escalation responses with predictive/diagnostic input where relevant.
Recommend where deeper analytical investment (tooling, data collection) would materially improve the SDM's risk visibility
Flag emerging risk patterns proactively to the SDM, even where not specifically requested


COMPETENCIES (KNOWLEDGE, SKILLS AND ATTRIBUTES)
Technical & Domain Competencies


Statistical & Quantitative Analysis - Working fluency in statistical methods (trend analysis, forecasting, variance analysis, correlation analysis) applied to operational data — not spreadsheet-level approximation
Data Modelling & Pipeline Design - Ability to design and maintain data models and automated pipelines that support analytical use cases, distinct from simple reporting extraction
Dashboard & BI Tooling Proficiency - Hands-on capability building and maintaining dashboards and visualisation tools (e.g. Power BI or equivalent) that translate complex data into decision-ready views
ITIL 4 Continual Improvement Practice - Understanding of how predictive analytics feeds into Continual Improvement and SLM processes, not analytics as a standalone technical exercise
XLA / Experience Data Literacy - Working knowledge of digital experience monitoring and sentiment analysis, and how experience-level data differs from and complements traditional SLA metrics
Data Handling Compliance - Understanding of compliance requirements where analytics involve personal or sentiment data, particularly in experience-level analysis


Analytical & Judgement Competencies


Signal vs Noise Discrimination - Reliably distinguishes genuine risk trends from normal operational variance; does not raise false alarms or miss early warnings
Correlation vs Causation Discipline - Never implies a causal relationship the data doesn't support; explicit about the limits of what the analysis shows
Forward-Looking Orientation - Defaults to predictive and diagnostic framing ("what's likely to happen") rather than purely descriptive reporting ("what happened")
Methodological Defensibility - Structures analysis so that methodology - not just the conclusion - can withstand scrutiny from a data-literate stakeholders
Data Quality Judgement - Recognises when data volume or quality is insufficient to support a reliable conclusion, and says so rather than presenting a weak signal as strong


Relationship & Influence Competencies


Technical-to-Business Translation - Converts statistical findings into business-impact language the SDM can act on, without requiring the SDM to interpret raw analytical output
Cross-Functional Coordination with the Business Analyst that works closely with the BA to keep analytics-layer data models aligned with reconciled source-of-truth data, avoiding duplicate or conflicting metrics
Proactive Risk Communication - Surfaces emerging risk patterns to the SDM unprompted, rather than waiting to be asked for analysis
Influence Through Evidence - Drives adoption of predictive insight into SDM decision-making through the strength and clarity of the analysis, not positional authority


Behavioural Competencies


Intellectual Rigour - Does not present a directional guess as a statistically supported conclusion; comfortable saying "the data doesn't support that yet"
Curiosity & Pattern-Seeking - Actively looks for patterns and anomalies in data rather than only analysing what's explicitly requested
Structured Documentation Habits - Maintains version control and documentation on analytical models and methodology, consistent with departmental audit and governance standards
Composure Under Technical Challenge - Able to defend methodology calmly and clearly when findings are questioned by technically sceptical stakeholders.


QUALIFICATIONS & EXPERIENCE
Minimum Qualifications


Bachelor's degree in data science, Statistics, Computer Science, Information Systems, or a related quantitative field (essential)
Formal training or certification in a BI/analytics tool (e.g. Power BI, SNOW) - essential
ITIL 4 Foundation certification - advantageous, given the role's alignment to Continual Improvement and SLM
Formal statistics, data analytics, or data science certification (e.g. relevant coursework, professional certification) - strongly preferred where not covered by degree specialisation


Minimum Experience


4 - 6 years in a Data Analyst, Business Intelligence Analyst, or Service Analytics role, ideally within an IT/Telecommunications or Managed Services environment (essential - pure academic/statistical background without applied service-data experience will require a steeper ramp-up)
Demonstrated experience building and maintaining dashboards and automated reporting pipelines that reduced manual reporting effort
Direct experience applying statistical or predictive analysis to operational data (incident trends, SLA performance, capacity data) - not purely descriptive reporting
Proven ability to identify and communicate early-warning risk signals that were later validated by operational outcomes
Experience working with large or messy operational datasets, including data quality assessment and structural gap identification