DDCS Business Analytics Data Lead & Portfolio Support

Key Responsibilities

• Develop, maintain, and continuously improve DDCS portfolio analytics and reporting that enable leadership decision-making.
• Pull, integrate, and validate data from relevant systems and sources (e.g., portfolio trackers, Smartsheet, Power BI datasets, extracts, and other approved repositories).
• Create clear definitions and governance for metrics (e.g., milestone health, resource/FTE views, throughput, cycle time, productivity indicators, and adoption/usage measures).
• Ensure data quality through reconciliation, standardization, and routine checks; document assumptions, definitions, and limitations.
• Convert raw data into actionable insights that explain what is happening, why it matters, and what leadership should do next.
• Identify trends, drivers, and outliers; perform root-cause exploration where appropriate.
• Translate analysis into implications for DDCS strategy, portfolio prioritization, resourcing, and operational execution.
• Be the DDCS portfolio data storyteller—creating digestible, leadership-ready narratives and visuals.
• Deliver concise readouts (written and visual) for leadership forums, including risks, trade-offs, and recommended actions.
• Tailor communication for different audiences (technical vs. business) while maintaining accuracy and clarity.
• Proactively identify program and portfolio risk signals and ensure they are visible early.
• Highlight risk areas such as schedule slippage, milestone readiness gaps, resource constraints, data integrity issues, and dependencies across functions.
• Recommend mitigation actions and track follow-through with owners as appropriate.
• Partner closely with DDCS Operations and cross-functional stakeholders to ensure consistent metrics and aligned reporting.
• Coordinate with Tech@Lilly, PRD, and Bengaluru data teams to enable consistency, reuse, and synergy where possible.
• Align outputs to broader standards and enterprise initiatives as applicable.
• Support additional initiatives and side projects as needed (e.g., process improvements, communications, AI adoption, and tools/systems implementation) to strengthen DDCS portfolio execution and decision-making.
• Design, collect, and communicate meaningful metrics to gauge and communicate progress (e.g., equivalent FTE saved).
• Proactively identify program issues/risks and recommend/implement solutions.

Basic Requirements

• Earned Bachelor’s degree in engineering, life sciences, business, analytics, or related field.
• 7+ years of experience in business analytics, portfolio analytics, operations analytics, data management, and/or program/portfolio reporting in a complex environment (regulated environment preferred).
• Demonstrated ability to independently pull data, analyze and translate it into business insights and recommendations.
• Demonstrated experience building executive-ready visuals and narratives (dashboards, scorecards, concise readouts, etc.).
• Proficient AI operating skills, including practical use (preferably with Copilot and/or Claude) to accelerate analysis, drafting, summarization, and stakeholder communications while maintaining appropriate judgment, confidentiality, and data integrity.
• Technical leadership: ability to speak both “IT” and “business” languages and translate requirements between stakeholders.

Additional Preferences

• Strong proficiency with analysis and reporting tools (e.g., Excel; Power BI or equivalent tools).
• Experience with data querying and preparation (e.g., SQL or similar), data modeling concepts, and working with large datasets.
• Comfort working across multiple data sources with imperfect inputs; ability to define data standards and improve quality over time.
• Ability to create and manage a recurring operating cadence for portfolio insights (weekly/monthly views), including metric definitions and refresh routines.
• Strong influencing skills with no direct authority; able to drive alignment through clarity, logic, and stakeholder partnership.
• Experience identifying and communicating risk in a structured way (risk signals, triggers, mitigations, decision points).
• Experience supporting AI enablement and/or analytics tool and system implementations (e.g., piloting, requirements definition, rollout communications, training, and adoption tracking).