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, governance, BI, analytics, data products, and AI, using internal talent and external partners as appropriate.
- Chair or co-lead a cross-functional Data and Analytics Council to prioritize investments, resolve ownership issues, and monitor value, quality, risk, and adoption.
- Develop business partnerships across Commercial, Market Access, Medical Affairs, R&D, Quality, Regulatory, Manufacturing, Supply Chain, Finance, Human Resources, and other functions.
- Manage vendors and strategic partners with clear accountabilities, performance measures, security expectations, and knowledge-transfer requirements.
Qualifications:
- Bachelor's degree in data science, analytics, statistics, computer science, information systems, engineering, business, or a related discipline; advanced degree preferred.
- Approximately 10 or more years of progressive experience in data, analytics, business intelligence, digital, technology, or related roles, including leadership of complex enterprise initiatives.
- Experience building or scaling enterprise data platforms, governance programs, BI capabilities, data products, advanced analytics, or AI-enabled solutions.
- Experience in pharmaceutical, biotechnology, healthcare, medical device, manufacturing, or another regulated industry strongly preferred.
- Demonstrated success leading cross-functional transformation, influencing senior stakeholders, managing partners, and converting strategy into measurable outcomes.
Technical and Business Capabilities
- Working knowledge of modern cloud data architectures, data integration, APIs, lakehouse or warehouse patterns, semantic models, BI platforms, metadata, lineage, master data, and data quality practices.
- Practical understanding of AI and machine learning lifecycle concepts, generative AI, model evaluation, responsible AI controls, monitoring, and human oversight.
- Ability to evaluate technology options and delivery approaches based on value, scalability, security, compliance, usability, interoperability, total cost, and organizational readiness.
- Strong business acumen, executive communication, portfolio management, financial discipline, vendor management, and change-leadership skills.
- Ability to explain complex data and AI topics clearly to technical, business, executive, and governance audiences.
The above statements are intended to describe the general nature and level of the work being performed by colleagues assigned to this position. This is not intended to be an exhaustive list of all responsibilities, duties, and skills required. Aquestive reserves the right to make changes to the job description whenever necessary.
As part of Aquestive’s employment process, final candidate will be required to complete a drug test and background check prior to employment commencing. Please Note: Aquestive is a drug-free workplace and has a drug free workplace policy in place.
Aquestive provides equal employment opportunities to all colleagues and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This position has an expected base salary range of $200,000 - $230,000.
