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 — Service Now 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
- Service Now (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 — Service Now 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
- Service Now (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.
- Explicit 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 — Service Now 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
- Service Now (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.

📌 PSR Modeler Offshore (Chennai)
🏢 Important Business
📍 Chennai