[All remote jobs](https://jobicy.com/jobs.md)[![Dropbox logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2020/09/WRILS-200923071227-398427.jpg)](https://jobicy.com/company/dropbox.md)[Dropbox](https://jobicy.com/company/dropbox.md) # Director, Data Science & Engineering, Finance AI Summary Dropbox is seeking a senior technical leader to manage a small data science and data engineering team supporting Finance. The director will set strategy, prioritize a portfolio spanning financial data foundations, forecasting models, analytics, and AI/ML use cases, and ensure reliable operational delivery. This role combines people leadership with technical judgment across data pipelines, model evaluation, governance, and production readiness. Success requires influencing Finance executives and cross-functional engineering, product, and data partners while operating autonomously in an evolving environment.[Apply for this job](#job-application)[View company](https://jobicy.com/company/dropbox.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 203k–309k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceDirectorPublished8 Nov 2026Apply before8 Dec 2026Listing views34Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. Opportunity details ## About this role. ### 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](https://www.dropbox.jobs/en/our-blog/employee-stories/the-era-of-durable-skills-what-the-data-tells-us-about-thriving-in-change/) 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 Show more [Apply now >](https://jobicy.com/jobs/159843-director-data-science-engineering-finance.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThe position requires leadership across two technical disciplines while owning finance-critical data, models, AI initiatives, and operating mechanisms. It also demands executive influence, high technical judgment, and effective prioritization amid ambiguity. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$256,000US market range (per year)$210k–$330k0$363.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses US base-compensation ranges of $203,000–$274,600 for Zone 3 and $228,400–$309,000 for Zone 2. The yearly cadence is inferred from the senior US director-level compensation scale because no explicit pay period is stated. The estimated US market range for a finance-focused data science and engineering director is $210,000–$330,000 annually. ## Core skills Skills and capabilities most closely associated with this opportunity. [Data science leadership](https://jobicy.com/jobs?search_keywords=Data%20science%20leadership.md)[Data engineering](https://jobicy.com/jobs?search_keywords=Data%20engineering.md)[Financial planning and analysis](https://jobicy.com/jobs?search_keywords=Financial%20planning%20and%20analysis.md)[Machine learning](https://jobicy.com/jobs?search_keywords=Machine%20learning.md)[Generative AI](https://jobicy.com/jobs?search_keywords=Generative%20AI.md)[Data governance](https://jobicy.com/jobs?search_keywords=Data%20governance.md)[Data quality](https://jobicy.com/jobs?search_keywords=Data%20quality.md)[Data pipelines](https://jobicy.com/jobs?search_keywords=Data%20pipelines.md)[Forecasting](https://jobicy.com/jobs?search_keywords=Forecasting.md)[Model evaluation](https://jobicy.com/jobs?search_keywords=Model%20evaluation.md)[People management](https://jobicy.com/jobs?search_keywords=People%20management.md)[Stakeholder management](https://jobicy.com/jobs?search_keywords=Stakeholder%20management.md)[Strategic prioritization](https://jobicy.com/jobs?search_keywords=Strategic%20prioritization.md)[Data observability](https://jobicy.com/jobs?search_keywords=Data%20observability.md)[Responsible AI](https://jobicy.com/jobs?search_keywords=Responsible%20AI.md) Sample interview questionsHow would you prioritize a Finance data team's work across recurring reporting, technical debt, and new AI opportunities?I would establish a transparent intake and prioritization process based on business impact, risk, effort, urgency, and strategic alignment. I would reserve explicit capacity for critical operations and reliability work, then use a quarterly roadmap to sequence high-value improvements and experiments. Describe how you would improve trust in financial datasets used for forecasting and executive reporting. I would identify the critical source-of-truth datasets, define data ownership and quality expectations, and implement testing, lineage, observability, and incident-response practices. I would also align Finance stakeholders on metric definitions and document assumptions so that reporting and models are auditable and consistently interpreted. What standards would you apply before deploying an AI or machine-learning solution in a Finance workflow? I would require a clearly defined decision use case, measurable success criteria, reliable input data, evaluation against appropriate baselines, and documented limitations. For sensitive workflows, I would include human oversight, privacy review, interpretability appropriate to the use case, monitoring, and a rollback or escalation plan. How do you lead effectively when your strongest technical background is in one discipline but your team spans data science and data engineering? I set clear outcomes and technical standards while relying on specialists for implementation depth. I ask rigorous questions about architecture, quality, evaluation, reliability, and trade-offs, create strong review mechanisms, and invest in learning enough of the adjacent discipline to make sound leadership decisions. How would you communicate uncertainty in a financial model to senior executives? I would focus on the decision implications rather than technical detail alone. I would present the central recommendation, key assumptions, confidence range, major risks, alternative scenarios, and the actions that would reduce uncertainty, using concise visuals and plain language. 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