Analytics Solution Engagement Manager (NCS/Job/ 3452)

Role Overview

The Analytics Solution / Engagement Manager will be responsible for leading end-to-end analytics engagements, from problem definition and solution design to delivery governance and client value realization. This role requires a strong blend of technical depth, analytics consulting, client management, and strategic storytelling, with exposure to AI-driven analytics and domain-specific solutions.

1. Education Background

• Strong academic foundation from IIT / NIT institutions.
• MBA from a top-tier B-school (IIMs, ISB, XLRI, SPJIMR, etc.) is required to ensure business acumen, strategic thinking, and executive client interaction capability.
• Candidate should demonstrate the ability to translate analytics outcomes into business and financial impact.

2. Hands-on Technical & Analytics Expertise

• Must possess hands-on experience (not just oversight) across:
  • Programming & Analytics: Python, PySpark, SQL
  • BI & Visualization: Power BI, Tableau
  • Data Engineering: ETL pipelines, cloud-based data processing, large-scale datasets
• Should be capable of reviewing, guiding, and validating technical design and analytics outputs from teams.
• Ability to bridge data engineering → analytics → insights → business decisions is critical.

3. Data Science & Advanced Analytics (Preferred)

• Exposure to data science modeling such as predictive, prescriptive, or statistical models is preferred.
• Should be able to guide model selection, interpret outputs, and position results for business stakeholders (even if not coding full models day-to-day).
• Understanding of model performance metrics, assumptions, and business applicability is expected.

4. Client Expectation Management & Analytics Storytelling

• Strong client-facing presence with the ability to manage expectations, scope, and outcomes.
• Excellent analytics storytelling skills—translating complex data insights into clear, actionable narratives for leadership audiences.
• Experience engaging with CXOs and senior business stakeholders across review forums.

5. Requirement Gathering & Cross-Practice Collaboration

• Extensive experience in structured requirement gathering, problem framing, and defining analytics perspectives aligned to business objectives.
• Able to work seamlessly in cross-practice or cross-functional setups (data engineering, BI, data science, domain teams).
• Comfortable in ambiguous environments, converting loosely defined business problems into well-defined analytics initiatives.

6. Proposals, Governance & Client Management

• Proven experience in:
  • Client proposals and solutioning (approach, architecture, effort estimation).
  • Project management and governance across large analytics programs.
  • Handling MBRs / QBRs, steering committees, and leadership updates.
• Strong capability to identify and resolve roadblocks independently through research, analysis, and stakeholder alignment.
• Prior exposure to RFI / RFP handling is highly preferred, including solution articulation and response ownership.

7. AI-Driven & Future-Oriented Mindset

• Demonstrates an AI-first or AI-driven mindset, leveraging AI/GenAI to enhance analytics efficiency, automation, and insights.
• Ability to identify AI use cases in analytics workflows such as:
  • Insight acceleration
  • Automated reporting
  • Advanced forecasting or decision support
• Awareness of emerging trends in analytics, AI, and data platforms.

8. Domain Expertise (Mandatory)

Strong hands-on data analytics domain knowledge in one or more of the following industries is mandatory:

• Utilities
• Construction
• Retail
• Textile / Packaging
• Supply Chain