PSR Modeler Offshore

Overview

The PSR SME owns the design and delivery of the Product & Service Record data model — the authoritative representation of how CUSTOMER's network products are structured and related to underlying infrastructure. The model underpins AI-driven event correlation, anomaly detection, root cause analysis, and service impact assessment across the AI Ops platform.

Key Responsibilities

PSR Model Design Design end-to-end PSR data model for in-scope products (IP Connect, MPLS, SD-WAN). Define PSR entities, attributes, relationships, and hierarchies. Develop generic model first; iterate to CUSTOMER-specific as data is confirmed. Validate model against current Catalog, Inventory and Ordering systems. Source System Analysis Analyse current Catalog, Inventory and Ordering systems for schemas, structures, and gaps.

Map data lineage from source systems to PSR model. Raise structured data requests to CUSTOMER stakeholders. Stakeholder & Workshop Engagement Lead PSR discovery workshops with CUSTOMER product and provisioning teams. Align with CMDB SME on CI-to-product mapping. Present findings and model designs to programme leadership.

Use Case Enablement Define data requirements per AI Ops use case across ingest, enrich, correlate, and present layers. Support event correlation and service impact design. Programme Delivery Author workstream deliverables; maintain RAID log. Contribute to JIRA stories and sprint planning. Ensure delivery aligns to PI features and sprint goals.

Key Deliverables 1 Generic PSR Data Model Product-agnostic entities, attributes & relationships. 2 CUSTOMER-Specific PSR Models Per-product models (IP Connect, MPLS, SD-WAN) validated against CUSTOMER data. 3 PSR Attribute Specification Sheet Attribute detail: type, source, transformation rule, mandatory flag. 4 Source System Analysis Report Current Catalog, Inventory and Ordering systems — schemas, gaps, quality findings.

5 Data Lineage Map (PSR) Source-to-PSR traceability for all key data attributes. 6 PSR–CMDB Integration Design How PSR entities map to CMDB CI classes. 7 Use Case Data Requirements Data needs per AI Ops use case at each delivery layer. 8 Gap Analysis & Recommendations Current state vs. PSR model requirements with prioritised actions.

9 PSR Workstream RAID Log Ongoing risks, assumptions, issues, and dependencies. Skills & Experience Domain Knowledge Telecom product & service structures (MPLS, IP Connect, SD-WAN). Product catalogue and service inventory data models in a telco OSS/BSS context. Network provisioning and fulfilment systems (billing gateways, order management).

CMDB data models and CI class design — ServiceNow preferred. AI Ops concepts: event correlation, anomaly detection, service impact, root cause analysis. Service assurance and fault management in network operations. Technical Skills Data modelling — entity-relationship, conceptual, logical, physical. SQL / database querying for schema analysis and validation.

Data mapping and transformation specification writing. Ability to read and interpret complex, under documented database schemas. JIRA — user story creation and sprint tracking. TMF SID certification - desirable ServiceNow (ITSM, CMDB, Event Management) — desirable. Kafka / event streaming awareness — desirable. Behavioural & Consulting Stakeholder engagement — extracting requirements from time-poor CUSTOMER SMEs.

Structured problem-solving in data-poor, ambiguous environments. Persistence in accessing and validating data from complex legacy systems. Clear communication — presenting data models to technical and non-technical audiences. Proactive risk identification and escalation. Comfortable with iterative, agile delivery and progressive elaboration.

Responsibilities

The PSR SME owns the design and delivery of the Product & Service Record data model — the authoritative representation of how CUSTOMER's network products are structured and related to underlying infrastructure. The model underpins AI-driven event correlation, anomaly detection, root cause analysis, and service impact assessment across the AI Ops platform.

Key Responsibilities

PSR Model Design Design end-to-end PSR data model for in-scope products (IP Connect, MPLS, SD-WAN). Define PSR entities, attributes, relationships, and hierarchies. Develop generic model first; iterate to CUSTOMER-specific as data is confirmed. Validate model against current Catalog, Inventory and Ordering systems. Source System Analysis Analyse current Catalog, Inventory and Ordering systems for schemas, structures, and gaps.

Map data lineage from source systems to PSR model. Raise structured data requests to CUSTOMER stakeholders. Stakeholder & Workshop Engagement Lead PSR discovery workshops with CUSTOMER product and provisioning teams. Align with CMDB SME on CI-to-product mapping. Present findings and model designs to programme leadership.

Use Case Enablement Define data requirements per AI Ops use case across ingest, enrich, correlate, and present layers. Support event correlation and service impact design. Programme Delivery Author workstream deliverables; maintain RAID log. Contribute to JIRA stories and sprint planning. Ensure delivery aligns to PI features and sprint goals.

Key Deliverables 1 Generic PSR Data Model Product-agnostic entities, attributes & relationships. 2 CUSTOMER-Specific PSR Models Per-product models (IP Connect, MPLS, SD-WAN) validated against CUSTOMER data. 3 PSR Attribute Specification Sheet Attribute detail: type, source, transformation rule, mandatory flag. 4 Source System Analysis Report Current Catalog, Inventory and Ordering systems — schemas, gaps, quality findings.

5 Data Lineage Map (PSR) Source-to-PSR traceability for all key data attributes. 6 PSR–CMDB Integration Design How PSR entities map to CMDB CI classes. 7 Use Case Data Requirements Data needs per AI Ops use case at each delivery layer. 8 Gap Analysis & Recommendations Current state vs. PSR model requirements with prioritised actions.

9 PSR Workstream RAID Log Ongoing risks, assumptions, issues, and dependencies. Skills & Experience Domain Knowledge Telecom product & service structures (MPLS, IP Connect, SD-WAN). Product catalogue and service inventory data models in a telco OSS/BSS context. Network provisioning and fulfilment systems (billing gateways, order management).

CMDB data models and CI class design — ServiceNow preferred. AI Ops concepts: event correlation, anomaly detection, service impact, root cause analysis. Service assurance and fault management in network operations. Technical Skills Data modelling — entity-relationship, conceptual, logical, physical. SQL / database querying for schema analysis and validation.

Data mapping and transformation specification writing. Ability to read and interpret complex, under documented database schemas. JIRA — user story creation and sprint tracking. TMF SID certification - desirable ServiceNow (ITSM, CMDB, Event Management) — desirable. Kafka / event streaming awareness — desirable. Behavioural & Consulting Stakeholder engagement — extracting requirements from time-poor CUSTOMER SMEs.

Structured problem-solving in data-poor, ambiguous environments. Persistence in accessing and validating data from complex legacy systems. Clear communication — presenting data models to technical and non-technical audiences. Proactive risk identification and escalation. Comfortable with iterative, agile delivery and progressive elaboration.

Requirements

The PSR SME owns the design and delivery of the Product & Service Record data model — the authoritative representation of how CUSTOMER's network products are structured and related to underlying infrastructure. The model underpins AI-driven event correlation, anomaly detection, root cause analysis, and service impact assessment across the AI Ops platform.

Key Responsibilities

PSR Model Design Design end-to-end PSR data model for in-scope products (IP Connect, MPLS, SD-WAN). Define PSR entities, attributes, relationships, and hierarchies. Develop generic model first; iterate to CUSTOMER-specific as data is confirmed. Validate model against current Catalog, Inventory and Ordering systems. Source System Analysis Analyse current Catalog, Inventory and Ordering systems for schemas, structures, and gaps.

Map data lineage from source systems to PSR model. Raise structured data requests to CUSTOMER stakeholders. Stakeholder & Workshop Engagement Lead PSR discovery workshops with CUSTOMER product and provisioning teams. Align with CMDB SME on CI-to-product mapping. Present findings and model designs to programme leadership.

Use Case Enablement Define data requirements per AI Ops use case across ingest, enrich, correlate, and present layers. Support event correlation and service impact design. Programme Delivery Author workstream deliverables; maintain RAID log. Contribute to JIRA stories and sprint planning. Ensure delivery aligns to PI features and sprint goals.

Key Deliverables 1 Generic PSR Data Model Product-agnostic entities, attributes & relationships. 2 CUSTOMER-Specific PSR Models Per-product models (IP Connect, MPLS, SD-WAN) validated against CUSTOMER data. 3 PSR Attribute Specification Sheet Attribute detail: type, source, transformation rule, mandatory flag. 4 Source System Analysis Report Current Catalog, Inventory and Ordering systems — schemas, gaps, quality findings.

5 Data Lineage Map (PSR) Source-to-PSR traceability for all key data attributes. 6 PSR–CMDB Integration Design How PSR entities map to CMDB CI classes. 7 Use Case Data Requirements Data needs per AI Ops use case at each delivery layer. 8 Gap Analysis & Recommendations Current state vs. PSR model requirements with prioritised actions.

9 PSR Workstream RAID Log Ongoing risks, assumptions, issues, and dependencies. Skills & Experience Domain Knowledge Telecom product & service structures (MPLS, IP Connect, SD-WAN). Product catalogue and service inventory data models in a telco OSS/BSS context. Network provisioning and fulfilment systems (billing gateways, order management).

CMDB data models and CI class design — ServiceNow preferred. AI Ops concepts: event correlation, anomaly detection, service impact, root cause analysis. Service assurance and fault management in network operations. Technical Skills Data modelling — entity-relationship, conceptual, logical, physical. SQL / database querying for schema analysis and validation.

Data mapping and transformation specification writing. Ability to read and interpret complex, under documented database schemas. JIRA — user story creation and sprint tracking. TMF SID certification - desirable ServiceNow (ITSM, CMDB, Event Management) — desirable. Kafka / event streaming awareness — desirable. Behavioural & Consulting Stakeholder engagement — extracting requirements from time-poor CUSTOMER SMEs.

Structured problem-solving in data-poor, ambiguous environments. Persistence in accessing and validating data from complex legacy systems. Clear communication — presenting data models to technical and non-technical audiences. Proactive risk identification and escalation. Comfortable with iterative, agile delivery and progressive elaboration.

The PSR SME owns the design and delivery of the Product & Service Record data model - the authoritative representation of how CUSTOMER's network products are structured and related to underlying infrastructure. The model underpins AI-driven event correlation, anomaly detection, root cause analysis, and service impact assessment across the AI Ops platform.

Key Responsibilities

PSR Model Design Design end-to-end PSR data model for in-scope products (IP Connect, MPLS, SD-WAN). Define PSR entities, attributes, relationships, and hierarchies. Develop generic model first; iterate to CUSTOMER-specific as data is confirmed. Validate model against current Catalog, Inventory and Ordering systems. Source System Analysis Analyse current Catalog, Inventory and Ordering systems for schemas, structures, and gaps.

Map data lineage from source systems to PSR model. Raise structured data requests to CUSTOMER stakeholders. Stakeholder & Workshop Engagement Lead PSR discovery workshops with CUSTOMER product and provisioning teams. Align with CMDB SME on CI-to-product mapping. Present findings and model designs to programme leadership.

Use Case Enablement Define data requirements per AI Ops use case across ingest, enrich, correlate, and present layers. Support event correlation and service impact design. Programme Delivery Author workstream deliverables; maintain RAID log. Contribute to JIRA stories and sprint planning. Ensure delivery aligns to PI features and sprint goals.

Key Deliverables 1 Generic PSR Data Model Product-agnostic entities, attributes & relationships. 2 CUSTOMER-Specific PSR Models Per-product models (IP Connect, MPLS, SD-WAN) validated against CUSTOMER data. 3 PSR Attribute Specification Sheet Attribute detail: type, source, transformation rule, mandatory flag. 4 Source System Analysis Report Current Catalog, Inventory and Ordering systems - schemas, gaps, quality findings.

5 Data Lineage Map (PSR) Source-to-PSR traceability for all key data attributes. 6 PSR-CMDB Integration Design How PSR entities map to CMDB CI classes. 7 Use Case Data Requirements Data needs per AI Ops use case at each delivery layer. 8 Gap Analysis & Recommendations Current state vs. PSR model requirements with prioritised actions.

9 PSR Workstream RAID Log Ongoing risks, assumptions, issues, and dependencies. Skills & Experience Domain Knowledge Telecom product & service structures (MPLS, IP Connect, SD-WAN). Product catalogue and service inventory data models in a telco OSS/BSS context. Network provisioning and fulfilment systems (billing gateways, order management).

CMDB data models and CI class design

• ServiceNow preferred. AI Ops concepts: event correlation, anomaly detection, service impact, root cause analysis. Service assurance and fault management in network operations. Technical Skills Data modelling - entity-relationship, conceptual, logical, physical. SQL / database querying for schema analysis and validation. Data mapping and transformation specification writing. Ability to read and interpret complex, under documented database schemas. JIRA - user story creation and sprint tracking. TMF SID certification - desirable ServiceNow (ITSM, CMDB, Event Management) - desirable. Kafka / event streaming awareness - desirable. Behavioural & Consulting Stakeholder engagement - extracting requirements from time-poor CUSTOMER SMEs. Structured problem-solving in data-poor, ambiguous environments. Persistence in accessing and validating data from complex legacy systems. Clear communication - presenting data models to technical and non-technical audiences. Proactive risk identification and escalation. Comfortable with iterative, agile delivery and progressive elaboration. The PSR SME owns the design and delivery of the Product & Service Record data model - the authoritative representation of how CUSTOMER's network products are structured and related to underlying infrastructure. The model underpins AI-driven event correlation, anomaly detection, root cause analysis, and service impact assessment across the AI Ops platform.

Key Responsibilities

PSR Model Design Design end-to-end PSR data model for in-scope products (IP Connect, MPLS, SD-WAN). Define PSR entities, attributes, relationships, and hierarchies. Develop generic model first; iterate to CUSTOMER-specific as data is confirmed. Validate model against current Catalog, Inventory and Ordering systems. Source System Analysis Analyse current Catalog, Inventory and Ordering systems for schemas, structures, and gaps.

Map data lineage from source systems to PSR model. Raise structured data requests to CUSTOMER stakeholders. Stakeholder & Workshop Engagement Lead PSR discovery workshops with CUSTOMER product and provisioning teams. Align with CMDB SME on CI-to-product mapping. Present findings and model designs to programme leadership.

Use Case Enablement Define data requirements per AI Ops use case across ingest, enrich, correlate, and present layers. Support event correlation and service impact design. Programme Delivery Author workstream deliverables; maintain RAID log. Contribute to JIRA stories and sprint planning. Ensure delivery aligns to PI features and sprint goals.

Key Deliverables 1 Generic PSR Data Model Product-agnostic entities, attributes & relationships. 2 CUSTOMER-Specific PSR Models Per-product models (IP Connect, MPLS, SD-WAN) validated against CUSTOMER data. 3 PSR Attribute Specification Sheet Attribute detail: type, source, transformation rule, mandatory flag. 4 Source System Analysis Report Current Catalog, Inventory and Ordering systems - schemas, gaps, quality findings.

5 Data Lineage Map (PSR) Source-to-PSR traceability for all key data attributes. 6 PSR-CMDB Integration Design How PSR entities map to CMDB CI classes. 7 Use Case Data Requirements Data needs per AI Ops use case at each delivery layer. 8 Gap Analysis & Recommendations Current state vs. PSR model requirements with prioritised actions.

9 PSR Workstream RAID Log Ongoing risks, assumptions, issues, and dependencies. Skills & Experience Domain Knowledge Telecom product & service structures (MPLS, IP Connect, SD-WAN). Product catalogue and service inventory data models in a telco OSS/BSS context. Network provisioning and fulfilment systems (billing gateways, order management).

CMDB data models and CI class design

• ServiceNow preferred. AI Ops concepts: event correlation, anomaly detection, service impact, root cause analysis. Service assurance and fault management in network operations. Technical Skills Data modelling - entity-relationship, conceptual, logical, physical. SQL / database querying for schema analysis and validation. Data mapping and transformation specification writing. Ability to read and interpret complex, under documented database schemas. JIRA - user story creation and sprint tracking. TMF SID certification - desirable ServiceNow (ITSM, CMDB, Event Management) - desirable. Kafka / event streaming awareness - desirable. Behavioural & Consulting Stakeholder engagement - extracting requirements from time-poor CUSTOMER SMEs. Structured problem-solving in data-poor, ambiguous environments. Persistence in accessing and validating data from complex legacy systems. Clear communication - presenting data models to technical and non-technical audiences. Proactive risk identification and escalation. Comfortable with iterative, agile delivery and progressive elaboration.