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.