The opportunity
This is a critical leadership role at the intersection of data science, data engineering, finance, and AI. As Director, Data Science & Data Engineering for Finance, you will lead a small, high-impact team that enables Finance to make better decisions through trusted data, robust…
What you'll do
Manage and grow a multidisciplinary team: Lead, coach, and develop a data scientist and a data engineer, creating clear expectations, meaningful development plans, effective delegation, and a strong culture of ownership and continuous improvement.
Set the team’s strategy and priorities: Translate Finance and company goals into a focused portfolio of work. Prioritize ruthlessly across strategic initiatives, recurring operational responsibilities, model maintenance, knowledge coordination, and technical debt.
Provide technical leadership across data science and data engineering: Guide problem framing, analytical and modeling approaches, data architecture, pipeline quality, testing, monitoring, documentation, and production readiness. You do not need to be the deepest expert in both disciplines, but you must be able to ask the right questions, recognize trade-offs, and raise the technical bar.
Advance AI and modeling capabilities: Identify high-value opportunities to apply AI, machine learning, automation, and advanced analytics to Finance workflows. Establish appropriate standards for experimentation, evaluation, interpretability, human oversight, privacy, and ongoing monitoring.
Build trusted data foundations: Partner with data engineering and Finance stakeholders to improve source-of-truth datasets, data quality, lineage, observability, documentation, and reliability. Ensure that recurring reporting, driver models, and downstream analyses are based on accurate and well-governed data.
Own analytical and modeling outcomes: Ensure the team develops, maintains, evaluates, and improves models and analyses that support financial planning, forecasting, business-driver understanding, and decision-making. Connect technical work to measurable business outcomes rather than treating model delivery as the finish line.
What they're looking for
- Bachelor’s degree or above in Computer Science, Data Science, Statistics,: Mathematics, Engineering, Economics, or a related quantitative discipline, or equivalent practical experience.
- Significant experience applying data science, machine learning, analytics,: data engineering, or a combination of these disciplines to business problems.
- Experience directly managing and developing technical professionals, ideally: including both data scientists and data engineers or closely adjacent roles.
- A strong professional foundation in either data science or data engineering,: with demonstrated ability to work effectively across the other discipline.
- Experience leading a portfolio of projects from problem definition through: delivery, adoption, measurement, and ongoing operation.
- Experience with data quality, data pipelines, analytical datasets, model: development or maintenance, experimentation or evaluation, and production or operational support.
- Demonstrated ability to use data and technology to influence business decisions and deliver measurable impact.
- Strong communication and stakeholder-management skills, including the ability: to explain technical concepts, uncertainty, and trade-offs to Finance and executive audiences.
