Head Of Delivery Intelligence

About the Company

Headquartered in New York, with a growing team in India (Noida), Daloopa is the leading provider of AI-powered fundamental data for institutional investors. Its proprietary platform sources, structures, and distributes the most complete and accurate historical financial datasetcovering nearly 4,700 public companies globally with more than 10 times the datapoints per company compared to other providers. Built for speed, auditability, and integration with AI tools, Daloopa helps hedge funds, private equity firms, mutual funds, corporates, and investment banks accelerate model-building and research workflows. The company recently secured $13M strategic investment to expand its AI-ready financial data platform. At Daloopa, we are driven by our values: Obsess over the customer, Raise the bar, be intensely proactive, and No BS. Joining us means being part of a culture that challenges you to think deeper, move faster, and make an impact every day.

About the Role

Mandate: Build a proactive analytics function that tells us what is breaking, why its breaking, and what to do about it before clients ever notice. We are looking for a Head of Delivery Intelligence to own the measurement, intelligence, and continuous improvement of Daloopas data delivery engine. This is not a traditional analytics roleit sits at the intersection of data operations, product intelligence, and strategic insight generation. You will build a proactive analytics function that doesnt just report on what happened but surfaces why it happened and what we should do about itdriving concrete action across Analyst teams, Technology, Product, and Engineering teams. You will be the internal authority on three non-negotiable pillars of Daloopas competitive moat: Speed, Accuracy, and Model Completeness.

Responsibilities

• Core Metrics & Measurement Framework
• Define, own, and continuously refine the metrics framework for data delivery across Speed, Accuracy, and Model Completeness (coverage depth, historical data quality).
• Build and maintain dashboards and scorecards that provide real-time and trend visibility to leadership and cross-functional stakeholders.
• Establish benchmarks and SLA thresholds that reflect best-in-class standards for Daloopa data.

• Proactive Insight Generation
• Deliver forward-looking, root-cause-rich reports that identify failure patterns, emerging risks, and improvement opportunities before they become client-visible issues.
• Analyze incident data (delivery delays, auto-tagging errors, model depth) to identify systemic trends and cluster actionable findings.
• Build a regular analytics cadence (weekly, monthly, quarterly) with crisp narratives and prioritized action items for Analyst teams, Tech, Product, and Engineering teams.

• Cross-Functional Action Enablement
• Translate analytical findings into clear, team-specific workstreams.
• Drive faster turnarounds in a complex, evolving multi-system data environment.
• Identify training gaps for analyst teams.
• Surface model edge cases for the ML team.
• Flag data pipeline bottlenecks.
• Recommend product feature enhancements.
• Partner with Engineering and Product to embed analytics-driven feedback loops into the production workflow.

• Client-Facing Quality Intelligence
• Develop analytics that map internal quality signals to client impact.
• Understand which accuracy or speed failures affect clients most acutely.
• Support client QBRs and reviews with structured, evidence-based quality reports.

• Thought Leadership & Standards
• Establish Daloopas internal point of view on what best-in-class looks like for financial data delivery.
• Champion a culture of measurement, accountability, and continuous improvement.
• Mentor and grow an analytics function over time as the team scales.

Qualifications

• Must-Have Experience
• 10+ years in analytics, data operations, or business intelligence, ideally high-scale B2B SaaS, fintech, financial data platforms, or AI/ML-driven product companies.
• Proven ability to build analytics programs from the ground up.
• Experience working closely with analyst teams / QA teams and Engineering or ML/AI product teams.
• Strong SQL, PowerBI skills, and aptitude for quickly mastering and leveraging new AI-powered analytics methodologies.
• Track record of translating complex data findings into clear, executive-ready narratives and actionable team recommendations.

• Mindset & Leadership
• Systems thinker who understands how human processes, ML models, and product decisions interact.
• Proactive builds detection infrastructure before clients surface problems.
• Able to operate deep in technical detail and strategic at the roadmap level.
• Credible cross-functional influencer.
• Ownership mentality toward data quality outcomes.

• Good To Have
• Familiarity with financial data (earnings models, KPIs, SEC filings, XBRL).
• Experience with incident management systems (Jira etc).
• Background supporting institutional investor clients.