Director, Data & Analytics

Aquestive Therapeutics, Inc. | Corporate Office
30 Technology Dr
Warren, NJ 07059, USA

Job Summary:

The Director, Data & Analytics leads the enterprise data, business intelligence, advanced analytics, and artificial intelligence agenda. The role defines strategy, establishes governance, builds scalable data capabilities, and partners with business leaders to convert trusted data into measurable business value. In a regulated life sciences environment, the Director ensures that analytics and AI solutions are secure, compliant, explainable, appropriately validated, and aligned with data integrity and privacy expectations.

Job Responsibilities

Enterprise Data and Analytics Strategy

• Define and execute a multi-year enterprise data and analytics strategy aligned with business priorities, technology architecture, risk tolerance, and the company operating model.
• Own the data and analytics portfolio, roadmap, operating cadence, budget recommendations, resource plan, and value-realization framework.
• Advise executive leaders on major data, analytics, digital, and AI opportunities, tradeoffs, dependencies, and risks.
• Create clear decision rights across central data teams, business data owners, stewards, and technology partners.

Data Platform, Architecture, and Delivery

• Lead the design and evolution of a secure, scalable enterprise data platform, including ingestion, integration, lakehouse or warehouse services, semantic models, and data products.
• Prioritize integration of critical commercial, clinical, quality, regulatory, manufacturing, financial, people, and enterprise data sources based on business value and readiness.
• Establish platform reliability, data freshness, monitoring, support, documentation, and service-level expectations.
• Apply disciplined product and program management to move initiatives from discovery through delivery, adoption, and measurable benefit.

Data Governance, Quality, and Compliance

• Establish the enterprise data governance framework, including domains, ownership, stewardship, definitions, metadata, cataloging, lineage, quality standards, access controls, retention, and issue management.
• Partner with Quality, Regulatory, Legal, Privacy, Information Security, and system owners to define controls appropriate to regulated and sensitive data use.
• Promote source-of-truth reporting, consistent enterprise KPI definitions, transparent assumptions, and audit-ready documentation.
• Ensure that data and analytics practices support applicable GxP, data integrity, privacy, security, records management, and validation requirements.

Business Intelligence and Decision Support

• Lead enterprise BI strategy, executive dashboards, performance reporting, self-service analytics, and common visualization and reporting standards.
• Partner with functional leaders to translate strategic and operational questions into data products, analyses, forecasts, scenarios, and actionable recommendations.
• Build an enterprise semantic and KPI layer that improves consistency, reuse, and confidence in management information.
• Drive adoption through training, data literacy, analyst communities, and embedded decision routines.

Artificial Intelligence and Advanced Analytics

• Define and execute an enterprise AI and advanced analytics roadmap focused on high-value, feasible, and responsible use cases.
• Establish an AI intake and prioritization process that evaluates business value, data readiness, model risk, regulatory impact, security, privacy, explainability, human oversight, and change-management needs.
• Lead responsible deployment of generative AI, copilots, predictive analytics, machine learning, intelligent automation, and decision-support solutions.
• Create and maintain AI governance standards for approved tools, acceptable use, access, documentation, testing, monitoring, model performance, bias and risk review, human-in-the-loop controls, and retirement.
• Partner with Quality, Security, Legal, Privacy, and business owners to determine when AI-enabled solutions require formal risk assessment, validation, control evidence, or ongoing monitoring.
• Ensure AI augments accountable human decision-making and is not used for unsupported autonomous decisions in regulated or high-risk processes.
• Build organizational AI fluency through practical education, use-case coaching, adoption support, and communication of benefits, limitations, and responsibilities.

Leadership, Talent, and Partnerships

• Build and lead a high-performing, multidisciplinary team spanning data engineering,…