# Director, Data Science & Engineering, Finance at Dropbox

> Remote · United States · Director+ · Analytics / Data science · open. Source: https://alderhq.app/jobs/greenhouse%3Adropbox%3A8356282 (read 2026-11-06).

## Facts (observed)

- Company: [Dropbox](https://alderhq.app/companies/dropbox)
- Location text: Remote - US: Select locations; Canada; US
- Where it can be done from (inferred): Remote · United States (evidence: "Location: Remote - US: Select locations; Canada; US")
- Posted: 2026-11-05 (1 days ago)
- First seen by alder: 2026-11-06 · last verified: 2026-11-06
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.dropbox.com/listing/8356282?gh_jid=8356282

## Pay and sponsorship

- Posted pay: $228k–$309k per year (from the listing's pay field)
- Market band for similar posted roles (United States, n 69): p25 $180k, median $220k, p75 $265k
- H-1B filings at this company for similar roles, FY26: 5, median offered wage $154k ([US DOL OFLC LCA disclosure data](https://www.dol.gov/agencies/eta/foreign-labor/performance))
- Licensed UK sponsor: registered as "Dropbox UK Online Limited" (checked 2026-11-03, [register](https://www.gov.uk/government/publications/register-of-licensed-sponsors-workers))

## Description (the employer's text)

### 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.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Adropbox%3A8356282 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.