S&C Global Network - Ai - Cmt - Telecom Analyst

Accenture; Bengaluru; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

Accenture is seeking a skilled Consultant Data Modeler to design and maintain data models supporting advanced analytics, AI/ML, and Generative AI use cases. The consultant will work closely with business stakeholders and data teams to translate complex business requirements into high-quality data models.

This role requires a Bachelor's or Master's degree in a related field and a minimum of 3 years' experience in data modeling. Strong collaboration skills and the ability to work independently are essential.

Qualifications

• 3+ years of hands-on experience in data modelling or data architecture roles.
• Experience designing conceptual, logical, and physical data models.
• Experience in consulting or client-facing roles is highly preferred.

Responsibilities

• Collaborate with stakeholders to understand data requirements.
• Design and maintain conceptual, logical, and physical data models.
• Support data governance initiatives including metadata management.

Skills

Data Modelling

Understanding of Knowledge Graphs

AI/ML Data Enablement

Conceptual Logical Design

Semantic Modelling

Education

Bachelors or Masters degree in Computer Science, Data Science, Information Systems, Engineering, or a related field

Tools

ER modeling tools

UML

Job description

Job Title - Decision Science Practitioner Consultant SC GN

Management Level:Consultant

Location:Bangalore/ Kolkata/Gurugram/Hyderabad

Must have skills:Data Modelling, Conceptual Logical Design, Semantic Modelling and Understanding of Knowledge Graphs


Good to have skills: Data Science, Ontologies, AI/ML Data Enablement, GenAI Foundations

Job Summary:

We are seeking a highly skilled and motivated Consultant Data Modeler to design, develop, and support the maintenance of conceptual, logical, physical, and semantic data models that support advanced analytics, AI/ML, and Generative AI use cases. The consultant will work closely with domain experts/business stakeholders, data science/engineering teams to translate complex business and system knowledge into scalable, high-quality data and semantic models. These models will serve as foundational assets enabling analytics, decision science, AI-driven insights, and intelligent automation across enterprise platforms. The role requires strong collaboration skills and the ability to work across multiple client environments.

Key Responsibilities


Project and Team Leadership

Collaborate with business stakeholders and domain experts to understand data requirements and translate them into clear conceptual, logical, and physical data models.

Work closely with data engineers, analytics teams, and AI/ML practitioners to ensure data models are aligned with downstream consumption needs.

Aptitude to work independently on modeling workstreams within client engagements, including requirements gathering, design, and documentation.

Document communicate modeling decisions, trade-offs, and best practices to both technical and non-technical audiences.

Contribute to project delivery through high-quality, low-defect delivery, documentation, design reviews, and model governance activities.

Data Modelling Semantic Modelling Expertise

Design and maintain conceptual, logical, and physical data models for structured and semi-structured data across enterprise systems.

Develop reusable, extensible data models supporting analytics, reporting, AI/ML feature engineering, and decision science use cases.

Apply semantic modeling techniques, including domain modeling, entity relationships, hierarchies, and taxonomies.

Enable ontology and knowledge graph modeling, translating subject-matter knowledge into machine-readable representations.

Ensure alignment of data models with enterprise architecture principles, data standards, and best practices.

AI GenAI Enablement

Design data and semantic models that act as foundational datasets for AI/ML and Generative AI systems.

Enable explainability, reasoning, and contextual grounding through ontology-driven approaches and graph-based data models.

Partner with data science and AI teams to ensure models support feature reuse, RAG pipelines, and intelligent agent workflows.

Contribute to data model designs that improve trust, interpretability, and scalability of AI-driven solutions.

Data Governance Standards

Follow and contribute to data modeling standards, naming conventions, and design guidelines across projects.

Support data governance initiatives including metadata management, lineage, and documentation.

Ensure consistency, quality, and reusability of data assets across platforms and use cases.

Business Impact and Innovation

Translate complex business concepts into clear, well-structured data models that accelerate analytics and AI adoption.

Enable faster solution development by providing high-quality, well-documented data foundations.

Support measurable outcomes by improving data usability, consistency, and decision-making effectiveness.

Qualification

Required
Qualifications

Experience

46 years of experience with 3+ years of hands-on experience in data modelling, information modelling, or data architecture roles.

Experience designing conceptual, logical, and physical data models.

Experience working in consulting or client-facing roles is highly preferred.

Education

Bachelors or Masters degree in Computer Science, Data Science, Information Systems, Engineering, or a related field.

Technical Skills

Strong expertise in data modeling techniques and methodologies.

Proficiency with data modeling tools (ER modeling tools, UML, or equivalent) is preferred.

Experience working with relational and graph-based data models.

Familiarity with semantic modeling concepts, ontologies, or knowledge graphs (e.g., RDF/OWL concepts).

Ability to collaborate effectively with data engineers, data scientists, and business stakeholders.

Preferred Skills

Exposure to knowledge graph technologies (e.g., Neo4j or similar).

Familiarity with AI/ML and GenAI consumption patterns, including feature stores, RAG pipelines, and agentic systems.

Experience defining data standards and governance frameworks.

Prior experience supporting analytics, decision science, or AI-driven initiatives.