Highlights:
4.00 – 6.00 Years
20.00 – 40.00 INR (Lacs)/Yearly
Full-time
Gurgaon, Bengaluru, Hyderabad
Skills
Data Modelling
Roles & Responsibility
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.
Required Qualifications
Experience
- 4–6 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
- Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems, Engineering, or a related field.
Requirements
Job Title – Decision Science Practitioner Consultant
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
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.