Global Data AI Strategic Partner

We are seeking a visionary data and analytics expert to lead our organization’s data strategy and drive advanced analytics initiatives. The Global Data & AI Strategic Partner we are looking for is an accomplished leader with a blend of deep technical expertise and strategic business insight. You will own the end-to-end development of data solutions – from data integration and warehousing through to advanced analytics, AI, and visualization – with the goal of unlocking business performance improvements and actionable insights. This role will interface with executive leadership to champion data-driven decision-making and ensure our data science and BI investments deliver high ROI.

• Data Strategy & Roadmap: Develop and continually refine a comprehensive data and AI strategy aligned with business objectives. Identify opportunities where data (analytics, machine learning, generative AI, etc.) can create competitive advantage, improve efficiency, or drive revenue growth.
• Leadership & Team Management: Lead and mentor a multidisciplinary team of data scientists, data engineers, BI analysts, and visualization specialists. Foster a culture of innovation, collaboration, and continuous learning, similar to how top leaders inspire passion for data within their teams. Set clear goals and high standards for data projects, and develop talent through coaching.
• Analytics Solution Delivery: Oversee the design and delivery of end-to-end analytics solutions. This includes guiding the architecture of data warehouses/lakes, ensuring data quality and governance, and implementing analytics platforms. Leverage experience with large-scale BI implementations to guarantee solutions are robust and scalable.
• Business Integration: Work closely with business units (finance, operations, marketing, etc.) to understand their needs and embed analytics into decision processes. Translate complex data findings into compelling business insights and recommendations for senior stakeholders. For example, use data visualization and storytelling techniques to communicate insights clearly. Ensure that analytics initiatives directly contribute to measurable business outcomes (cost savings, performance gains, etc.)
• Innovation & AI Oversight: Stay at the forefront of emerging technologies in data science and AI. Evaluate and implement multi-vendor AI solutions and advanced analytics tools appropriate for our business context Guide the organization in exploring new frontiers like generative AI for business, while balancing innovation with practicality. Make recommendations on build vs. buy for analytics tools and keep our data stack modern.
• Governance & Trust: Establish strong data governance practices and ethical AI guidelines. Ensure data integrity, security, and compliance in all analytics activities. Champion transparency in AI models and algorithms to build trust in automated insights. For instance, put in place processes to explain AI-driven decisions (addressing the "black box" issue so stakeholders trust the outcomes).
• Cross-Functional Collaboration: Collaborate with IT, product development, and external partners or vendors. Drive partnerships that enhance our data capabilities (e.g., consulting with providers, participating in industry forums). Act as the internal evangelist for data-driven culture, educating other departments on how to leverage data effectively.
• Performance Monitoring: Define key performance indicators (KPIs) for data initiatives and regularly report on their impact. Use a results-oriented approach reminiscent of our benchmark leaders – focusing on how analytics initiatives improve business metrics and adjusting strategy accordingly.

• You have a Ph.D. in Data Science, Computer Science, Statistics, or related field – or – a Master’s in Engineering/Computer Science plus an MBA (or equivalent advanced business degree).
• You have 15+ years of relevant experience in data analytics, business intelligence, or data science roles, with at least 5 years in senior leadership positions (e.g., Head of Analytics, Director of BI, Chief Data Officer, etc.). Proven track record of driving data projects from concept to successful rollout is essential. Your experience span multiple industries or business environments, demonstrating adaptability and broad perspective.
• You have an expert-level knowledge of data analytics technologies: data warehousing, ETL, big data platforms, and advanced analytics methods (statistical modeling, machine learning). You are familiar with modern AI/ML frameworks and cloud data stacks. Hands-on ability with BI tools and data visualization software (Power BI, Tableau, etc.) is important. You are capable of reviewing code/algorithms and guiding technical teams when needed.
• You have strong business acumen with ability to align data initiatives to business strategy. You are capable of identifying high-impact use cases for analytics (e.g., customer analytics, operational optimization, risk modeling). Experience in consultancy or stakeholder-facing roles is a plus, as it indicates ability to gather requirements and deliver value to different parts of an organization.
• You have demonstrated leadership in building and managing high-performing teams. Excellent communication and data storytelling skills – able to present complex analyses in a clear, compelling way to executive audiences. You inspire confidence and enthusiasm about what data can do for the company, much like the thought leaders we benchmarked.
• You adept at project planning and execution in the context of data initiatives. You have overseen multiple projects simultaneously, delivering on time and within budget. Familiarity with Agile methodologies in data projects is beneficial.
• You are a proactive learner who stays current with emerging trends in AI and analytics. You have a history of implementing innovative solutions (for example, piloting new AI techniques or adopting new tools ahead of peers). You encourage experimentation and creative problem-solving in your team to continuously improve data practices.
• You have a deep understanding of data privacy and AI ethics. Commitment to using data responsibly and maintaining high standards of data quality and security. You are able to put in place checks so that analytics are fair, transparent, and trustworthy.