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3 Days ago

Senior Business Analyst

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Mumbai, Maharashtra, India
Information Technology
Full-Time
Tarento Group

Overview

About Tarento

Tarento is a fast-growing technology consulting company headquartered in Stockholm, with a strong presence in India and clients across the globe. We specialize in digital transformation, product engineering, and enterprise solutions, working across diverse industries including retail, manufacturing, and healthcare. Our teams combine Nordic values with Indian expertise to deliver innovative, scalable, and high-impact solutions.

We're proud to be recognized as a Great Place to Work, a testament to our inclusive culture, strong leadership, and commitment to employee well-being and growth. At Tarento, you’ll be part of a collaborative environment where ideas are valued, learning is continuous, and careers are built on passion and purpose.

Role Overview

We are looking for a Senior Business Analyst who can bridge strategy, product, data, and engineering to drive impactful AI solutions from concept to scale.

This role requires someone who understands AI systems, data pipelines, platform thinking, and stakeholder ecosystems, and can translate complex business needs into structured, measurable, and scalable product requirements.

Key Responsibilities

Strategic Business Analysis

    • Work with leadership to define platform vision, OKRs, and measurable business outcomes.
    • Translate high-level strategy into structured BRDs, PRDs, and AI use-case documentation.
    • Identify monetization, adoption, and ecosystem growth levers.
AI & Data Requirement Definition

  • Define use cases for:
    • Language AI systems
    • Observability portals
    • Voice-based AI interfaces
    • Agentic AI workflows
  • Collaborate with Data Science teams to:
    • Define data requirements
    • Structure labeling guidelines
    • Evaluate model performance metrics
    • Frame AI evaluation criteria (accuracy, recall, latency, bias, drift, etc.).
Stakeholder & Ecosystem Management

  • Work with:
    • Government stakeholders
    • NGOs / ecosystem partners
    • Enterprise clients
    • Product & Engineering teams
    • Conduct workshops, requirement discovery sessions, and user journey mapping.
    • Convert ambiguous problem statements into structured solution frameworks.
Product & Platform Collaboration

  • Work closely with Product Managers and TPMs to:
    • Prioritize roadmap items
    • Define MVP vs Scale releases
    • Conduct impact analysis
    • Support release planning and sprint readiness.
    • Participate in UAT and acceptance testing.
Data-Driven Insights & Reporting

  • Build dashboards and KPI frameworks for:
    • Platform adoption
    • AI model performance
    • User engagement
    • Revenue metrics (if applicable)
    • Present insights to leadership with structured executive summaries.
Experience

Required Skills & Experience

    • 6–10+ years of experience in Business Analysis, Product Analytics, or Consulting.
    • Experience working with AI/ML-based products (must-have).
    • Exposure to government, public sector, or large ecosystem programs preferred.
    • Experience in platform-based or SaaS models preferred.
Technical Understanding

    • Strong understanding of:
    • AI/ML lifecycle
    • Data pipelines
    • APIs & microservices
    • Cloud platforms (AWS/Azure/GCP)
    • Ability to interpret model performance metrics.
    • Experience with SQL, dashboards (Power BI/Tableau), or analytics tools preferred.
Core Competencies

    • Structured problem solving
    • Systems thinking
    • Executive communication
    • Stakeholder influence
    • Documentation excellence
    • Ability to operate in ambiguity
Preferred Qualifications

    • MBA or equivalent business/analytics background.
    • Certifications in Business Analysis (CBAP/PMI-PBA) preferred.
    • Experience in Language AI, Voice AI, or Digital Public Infrastructure is a plus.
Success Metrics (First 6–12 Months)

    • Clear documentation of use-case repository.
    • Improved requirement clarity → reduced rework in sprints.
    • AI model performance reporting standardized.
    • Stakeholder satisfaction improvement.
    • Contributed to measurable adoption or revenue growth.
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