Senior Director – Data & Analytics (Digital Solutions, India)

Key Outcomes

• Service & Solution Portfolio: Shape an impactful services and solutions portfolio and ensure robust, future‑ready technology foundations.
• Products & Use Cases: Deliver a continuous of AI/analytics products (e.g., Process/Agentic AI, Smart Automation) with business OKRs and ROI baselines.
• Architecture & partnerships: Institutionalize reference architectures and leverage strategic ecosystems (e.g., Snowflake) for scale and reuse.
• People & Culture: Build a high‑performing, capability‑rich D&A organization; strengthen engagement, retention, and continuous learning.
• Governance & compliance: Embed data security, privacy, and audit readiness across solutions
• Platform & Migration: Deliver prioritized waves of Siemens Data Cloud migration; operationalize high‑availability data services with clear SLOs.
• Thought Leadership & Influence: Act as a recognized expert in Data & AI across Siemens, contributing thought leadership, best practices, and insights.
• AI Strategy & Operating Model: Support and evolve the AI operating model, ensuring responsible, scalable, and business‑aligned AI adoption.
• Value Realization & ROI: Define and deliver clear, measurable business impact targets from Data & AI initiatives.

Responsibilities 1) Strategy & Portfolio

• Define India D&A strategy tied to DS priorities (Horizontal AI, Digital Experience, Data Factory, RPA/Automation) and annual investment plan.
• Maintain a living roadmap of platforms, products, and services, balancing build‑vs‑buy via DS ecosystems (e.g., Snowflake).
• Provide thought leadership that shapes enterprise‑wide Data & AI direction

2) Product & Delivery Leadership

• Establish and scale a product‑centric operating model connected to business outcomes and OKRs.
• Lead multidisciplinary squads across business, analytics, engineering, and TechOps for end‑to‑end delivery excellence
• Drive AI adoption across business units, ensuring alignment to enterprise strategy and measurable business impact
• Own and evolve the enterprise AI operating model to ensure responsible and scalable deployment

3) Data Platforms & Architecture

• Own India‑level data platforms: ingestion, storage, transformation, serving; set canonical models and quality SLAs.
• Lead Siemens Data Cloud migration waves, interoperability and cost optimization.

4) Advanced Analytics, AI & Automation

• Industrialize ML lifecycle (feature stores, MLOps, monitoring, model risk & bias checks).
• Scale Process/Agentic AI and automation programs that deliver measurable productivity and operational gains.
• Ensure AI and GenAI initiatives are tied to real business outcomes and adoption.
• Define and deliver clear financial impact targets (productivity, cost optimization, revenue enablement) from Data & AI solutions.
• Lead adoption of multi‑agent and agentic AI systems for decision intelligence and automation

5) Governance, Cybersecurity & Compliance

• Partner with GCI to embed security, guardrails, and audit readiness; align to ISO and Siemens policies.
• Oversee data cataloging, classification, lineage, and access governance for all India‑owned platforms.
• Ensure responsible AI governance and risk management across all AI deployments.

6) People Leadership & Organization Design

• Lead ~100‑member team; define career architecture, learning paths, and succession; maintain a healthy FTE/FTC/vendor mix.
• Promote “empowered people” culture and continuous certification momentum.
• Build AI literacy and Data/AI fluency across teams through structured upskilling and leadership enablement.
• Support senior leaders on how to frame AI use cases, evaluate AI risks, and translate business goals into AI-ready opportunities.

7) Stakeholder & Financial Management

• Communicate effectively with senior executives, business leads, and global partners.
• Translate business needs into OKR‑aligned commitments supported by strong delivery governance.
• Own budgets, vendor strategy, and productivity targets; leverage DS ecosystems for speed and scale.
• Drive measurable value realization and financial impact from Data & AI initiatives Service & Solution Portfolio Leadership
• Shape and evolve service and solution portfolio to maximize business impact and customer value.
• Review, refine, and modernize the technology stack and architectural building blocks underpinning DS services.
• Ensure alignment with global platform strategy, reuse opportunities, and scalable enterprise patterns.

Candidate Profile Essential Qualifications

• 10–15+ years in data/analytics/AI with at least 5 years leading large (75+) engineering & data teams.
• Proven ownership of enterprise data platforms and analytics product portfolios in a global matrix environment.
• Experience migrating to cloud‑native data stacks and scaling MLOps.

Technical Depth (representative)

• Cloud & data platforms: Azure/AWS;

Snowflake (strategic in DS), Databricks or equivalent; modern ETL/ELT; event streaming.

• Analytics & AI: Feature stores, ML pipelines, model monitoring; GenAI/Agentic patterns tied to business outcomes.
• BI & orchestration: Power BI, orchestration frameworks; data quality, cataloging, lineage.
• Modern AI Stack: Semantic layers, vector databases, LLMOps, orchestration frameworks, multi‑agent systems.

Leadership

• Product/Service mindset with strong outcome orientation and OKR expertise.
• Excellent stakeholder management; builds high‑trust, cross‑regional collaboration.
• Strong project execution discipline and pragmatic governance mindset.
• Passion for talent development, inclusion, and building strong engineering/AI cultures.
• Ability to act as an enterprise thought leader and influence Data & AI direction.
• Governance and risk management; pragmatic approach to guardrails and reuse.