UST
Website:
ust.com
Job details:
Role Description
Senior Program Director - Data, AI & Decision Systems (Enterprise / Regulated Environment)
Location: Hyderabad, India
Function: Enterprise Technology / Data & AI Transformation
Work Model: Office
Role Mandate (Non-Negotiable)
This is not a coordination role.
You are accountable for
turning Data & AI investments into production-grade, financially accountable, business-critical systems operating at scale in a
high-dependency, regulated enterprise environment.
You will own
end-to-end program outcomes, not delivery activities including
technical viability, adoption, and measurable business impact.
Failure Is Defined As
- Models that do not reach production
- Platforms that are not adopted
- Investments that do not translate into business value
Scope of Ownership
You will lead
multi-year, multi-million-dollar Data & AI programs spanning:
- Enterprise data platforms (lakehouse, data mesh, governed pipelines)
- ML/AI products (predictive, prescriptive, decision automation systems)
- Cross-system integrations (APIs, event-driven architectures)
- Business-critical analytics and decision workflows
Programs Will Typically Involve
- 5-10+ cross-functional teams
- Distributed/global stakeholders
- High regulatory or data sensitivity constraints
Core Accountability Areas
- End-to-End Outcome Ownership
- Own delivery from problem framing architecture alignment deployment adoption value realization
- Ensure traceability between business case, technical delivery, and realized outcomes
- Drive production-grade readiness, not prototype success
- Technical Program Leadership (Data & AI Systems)
You are not expected to build models, but you
must challenge them credibly.
Operate Fluently Across
- Data engineering (ETL/ELT, batch vs streaming, data contracts)
- ML lifecycle (training, validation, drift, retraining strategies)
- Deployment patterns (MLOps, CI/CD, model serving)
- Platform constraints (cloud architecture, scalability, latency)
Mandate: Identify and intervene in:
- Weak architectures
- Unrealistic delivery sequencing
- Hidden technical dependencies
- Data quality risks that invalidate outcomes
- Financial & Commercial Accountability
- Own CapEx / OpEx planning, forecasting, and variance control
- Challenge and validate business case assumptions
- Ensure ROI realization and benefit tracking post-deployment
- Make trade-off decisions between cost, speed, and quality
- Execution Control in High-Ambiguity Environments
- Drive execution across uncertain, evolving problem spaces
- Maintain control across:
- Scope volatility
- Cross-team dependencies
- Resource contention
- Establish predictability in inherently unpredictable environments
- Dependency & Systems Integration Leadership
- Orchestrate delivery across:
- Data sources (internal/external)
- Platforms and shared services
- Upstream/downstream consumers
- Resolve critical path conflicts and sequencing risks
- Stakeholder & Executive Alignment
- Operate at Director / VP level stakeholders
- Translate technical trade-offs into business implications
- Drive alignment where incentives and priorities conflict
- Influence decisions without direct authority
- Governance & Risk Ownership
- Own program governance including:
- RAID management
- Steering forums
- Escalation frameworks
- Proactively identify:
- Delivery risks
- Model risk (bias, drift, reliability)
- Data compliance issues
Required Experience (Strict Interpretation)
- 10-15+ years in program leadership roles (not project coordination)
- Demonstrated ownership of end-to-end Data or AI programs in production
- Proven delivery in complex, multi-system environments with dependencies
- Direct engagement with:
- Data Engineering teams
- Data Science / ML teams
Must Show Evidence Of
- Decision authority (not just facilitation)
- Business impact (quantified outcomes)
- Scale (enterprise or multi-region programs)
Technical Depth Expectations (Minimum Bar)
You Must Demonstrate Working Understanding Of
- Data lifecycle: ingestion transformation consumption
- ML lifecycle: experimentation validation deployment monitoring
- Data governance: quality, lineage, compliance constraints
- Cloud ecosystems: AWS / Azure / GCP (architecture-level understanding)
Preferred (High-Value Signals)
- Experience in regulated industries (pharma, healthcare, finance, etc.)
- Exposure to global operating models / matrix organizations
- Experience recovering failing or high-risk programs
- Certifications (secondary signal only): PMP, PgMP, Agile
Success Profile (What Top 10% Looks Like)
A Strong Hire Will Demonstrate
- Delivered AI/ML programs that reached production and scaled
- Intervened in and corrected technically flawed delivery plans
- Owned budget + outcome accountability simultaneously
- Navigated high ambiguity without loss of delivery control
- Earned trust of senior leadership for critical decisions
Failure Patterns (Automatic Red Flags)
- Supported or coordinated AI initiatives without ownership
- Delivery limited to dashboards or analytics (no production AI)
- No evidence of financial accountability
- Technical discussions limited to tools (no system understanding)
- Programs delivered without measurable business outcomes
Skills
data engineering,gcp architecture,azure platform,artificial intelligence,ai system,cloud computing,
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