Strategic Overview
Director, Data Science & Engineering, Finance at Dropbox is defined by the responsibilities and candidate requirements in the employer's listing.
The published role emphasizes work such as 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.
The role is listed for United States with a remote working arrangement, giving candidates the practical context needed to assess fit before applying.
Key information at a glance
About This Role
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 analytical systems, applied modeling, and increasingly AI-powered capabilities.
You will manage two direct reports: a data scientist and a data engineer.
Key Responsibilities & Objectives
- 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.
- 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.
- Create scalable operating mechanisms: Establish lightweight processes for intake, prioritization, roadmap planning, model and pipeline reviews, incident response, documentation, knowledge sharing, and stakeholder communication.
- Influence Finance and senior stakeholders: Communicate complex data, model, and engineering considerations clearly to technical and non-technical audiences. Present implications, recommendations, risks, and trade-offs in a way that enables timely decisions.
- Partner across functions: Work effectively with Finance leaders, analysts, data scientists, data engineers, software engineers, product partners, and executives. Seek diverse perspectives, lead productive debate, and align stakeholders when priorities or approaches differ.
- Improve the team’s scope and effectiveness: Look continuously for ways to eliminate low-value work, automate recurring tasks, improve service levels, and increase the team’s strategic contribution. When the current operating model is insufficient, make the case for change and lead the team through it
Required Qualifications & Candidate Blueprint
- 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.
- Demonstrated growth mindset: actively seeks feedback, learns new technologies and domains, adapts to changing conditions, and creates an environment where the team does the same.
- Ability to operate with a high degree of autonomy and ownership in an ambiguous, fast-changing environment.
Preferred Qualifications
- Experience supporting Finance, financial planning and analysis, forecasting, corporate strategy, accounting, or other business functions with high standards for accuracy and control.
- Experience building or scaling AI/ML capabilities, including generative AI, predictive modeling, model evaluation, workflow automation, or AI-assisted decision support.
- Experience managing a hybrid or multidisciplinary team spanning data science, analytics, data engineering, or software engineering.
- Experience building 0-to-1 capabilities, establishing new operating models, or transforming a team from reactive execution to proactive partnership.
- Experience with data governance, observability, model risk management, responsible AI, or other practices that support reliable and auditable business decisions.
- Experience working in a technology company or other complex, data-rich environment.
Salary & Benefits
Published compensation: USD 203,000 – 309,000 / yearly
Salary Benchmark
This role’s published compensation is compared with active employer-disclosed ranges for the same role at the Director level, in the same currency and pay period.
Benchmark uses active employer job postings in our database, excludes this listing, and matches the currency and pay period. It is not a third-party salary estimate; currencies are not converted.
Full Job Description
The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.
Role Description
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 analytical systems, applied modeling, and increasingly AI-powered capabilities.
You will manage two direct reports: a data scientist and a data engineer. Together, the team supports the financial organization across recurring business operations, driver-model maintenance, knowledge coordination, analytical insights, and the development and operationalization of AI and machine-learning use cases. The role requires enough technical depth to guide both disciplines, while maintaining a clear focus on business outcomes, reliability, and team growth.
You will partner closely with Finance leadership, Engineering, Data, Product, and other cross-functional stakeholders to establish priorities, improve the quality and accessibility of financial data, and shape a forward-looking roadmap. You will be expected to move the team beyond request-driven execution: identifying the highest-leverage opportunities, clarifying the problems worth solving, and creating the operating mechanisms needed to deliver durable impact.
This is an opportunity to shape how Finance uses data and AI at a time when the underlying technologies, operating models, and expectations are changing quickly. The successful candidate will bring a growth mindset, a strong background in either data science or data engineering, and the ability to lead effectively across both disciplines.
Responsibilities
- 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.
- Create scalable operating mechanisms: Establish lightweight processes for intake, prioritization, roadmap planning, model and pipeline reviews, incident response, documentation, knowledge sharing, and stakeholder communication. Reduce dependency on individual heroics and make the team’s work easier to understand and extend.
- Influence Finance and senior stakeholders: Communicate complex data, model, and engineering considerations clearly to technical and non-technical audiences. Present implications, recommendations, risks, and trade-offs in a way that enables timely decisions.
- Partner across functions: Work effectively with Finance leaders, analysts, data scientists, data engineers, software engineers, product partners, and executives. Seek diverse perspectives, lead productive debate, and align stakeholders when priorities or approaches differ.
- Improve the team’s scope and effectiveness: Look continuously for ways to eliminate low-value work, automate recurring tasks, improve service levels, and increase the team’s strategic contribution. When the current operating model is insufficient, make the case for change and lead the team through it
Requirements
- 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.
- Demonstrated growth mindset: actively seeks feedback, learns new technologies and domains, adapts to changing conditions, and creates an environment where the team does the same.
- Ability to operate with a high degree of autonomy and ownership in an ambiguous, fast-changing environment.
Preferred Qualifications
- Experience supporting Finance, financial planning and analysis, forecasting, corporate strategy, accounting, or other business functions with high standards for accuracy and control.
- Experience building or scaling AI/ML capabilities, including generative AI, predictive modeling, model evaluation, workflow automation, or AI-assisted decision support.
- Experience managing a hybrid or multidisciplinary team spanning data science, analytics, data engineering, or software engineering.
- Experience building 0-to-1 capabilities, establishing new operating models, or transforming a team from reactive execution to proactive partnership.
- Experience with data governance, observability, model risk management, responsible AI, or other practices that support reliable and auditable business decisions.
- Experience working in a technology company or other complex, data-rich environment.
Durable Skills
AI fluency means using these tools to amplify human judgment, not replace it. We believe people with these skills will thrive as work and technology continue to evolve:
- Awareness: Understand yourself and others.
- Judgment: Evaluate information and make decisions in complex situations.
- Adaptability: Learn, adjust, and stay effective through change.
- Connection: Communicate, collaborate, and build trust.
To learn more about why these skills matter and what the data shows about thriving through change, read this blog post from our Chief People Officer, Melanie Rosenwasser.
Compensation
US Zone 1
This role is not available in Zone 1
US Zone 2
$228,400—$309,000 USD
US Zone 3
$203,000—$274,600 USD
Editor’s Market Perspective (Our Custom Analysis)
Why This Position Matters
This opening may suit candidates who can demonstrate experience relevant to the employer's published requirements. Its strongest career signal is the scope of the work itself — particularly 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. — rather than a generic title alone.
Resume & Application Strategy
For the application, connect specific examples from your experience to the responsibilities and qualifications in the employer's description. Quantify outcomes from comparable work, and place the most relevant evidence near the top of the resume. When the employer explicitly names tools, platforms, leadership scope, or domain experience, use those source terms accurately and in context.
We organize source-provided information into this independent job-seeker overview. Compensation is shown only when the source provides it; the official listing remains the authority for current requirements and application instructions.