Senior Data Scientist - Growth

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In the 1840s, Charles Babbage and Ada Lovelace worked on an early version of the computer known as the “Analytics Engine”. In the words of computing historian Doron Swade, “What Lovelace saw...was that numbers could represent entities other than quantity” and together they laid the foundation for general-purpose computing. While much has changed since then, the importance of numbers in building great technology remains. We’re looking for Data Scientists who can help us build our analytics engine by making meaning from data and identifying opportunities for improvement. As a Growth Data Scientist at Mercury , you will partner with Growth product, design, engineering and marketing leaders to shape how Mercury acquires, activates and retains customers across business and personal banking*. This is a strategic, product-facing role: you’ll frame the “why” behind what we build, size opportunities, and turn the growth funnel into clear, prioritized recommendations. You will work as a full-stack Data Scientist on projects end to end and build deep domain expertise at the intersection of Data Science, Product and Growth. You will help set the direction for how Growth measures success and decides where to invest, in line with Mercury ’s broader product and company goals. Here are some things you’ll do on the job: Partner with Growth product, design, engineering and marketing leaders to identify the highest-impact business questions, and influence what gets built and why. Own the growth funnel end to end, from first visit through signup, approval, activation and retention, building the metrics, driver trees and narratives that show where we’re winning and where we’re not. Size opportunities and help prioritize the Growth roadmap and annual planning, making clear recommendations even when data is incomplete. Design, analyze and communicate experiments (A/B tests and quasi-experimental methods) that tell teams whether a change worked and what to do next. Right-size your work to the decision: fast and directional when that’s what’s needed, rigorous when the stakes call for it. Collaborate with other Data Scientists, Data Engineers and Analytics Engineers to build trusted, self-serve growth metrics. Communicate findings and recommendations to audiences at all levels, including executives, with a clear point of view that improves entrepreneurs’ lives and generates revenue for Mercury . You should: Have 6+ years of experience in data science, product analytics or growth analytics, including 3+ years partnering closely with product growth teams. Have strong product sense and a track record of shaping product direction, not just measuring it, including times your work changed what a team built. Have deep experience with experiment design and analysis, as well as funnel, cohort and retention analysis. Have fluency in SQL and a statistical programming language (e.g. Python, R). Have experience with product analytics and BI tools such as Amplitude, Hex, Omni or similar; familiarity with Snowflake and dbt is a plus. Be comfortable with ambiguity and with respectfully disagreeing with senior stakeholders, backed by evidence. Be a clear, concise communicator who leads with the point and tailors the message to technical and non-technical audiences. Nice to have: experience in consumer or SMB fintech, or with mobile product analytics. * Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC. Mercury values diversity belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applica

在19世纪40年代，查尔斯·巴贝奇和艾达·洛夫莱斯致力于早期计算机的研发，即所谓的“分析机”。用计算历史学家多伦·斯韦德的话来说，“洛夫莱斯所看到的是……数字可以代表数量之外的实体”，他们共同为通用计算奠定了基础。尽管自那时以来发生了许多变化，但数字在构建伟大技术中的重要性依然不变。我们正在寻找能够帮助我们构建分析引擎的数据科学家，通过从数据中提炼意义并识别改进机会。作为Mercury的增长数据科学家，你将与增长产品、设计、工程和营销负责人合作，塑造Mercury如何在商业和个人银行*领域获取、激活和留住客户。这是一个战略性的、面向产品的角色：你将构建我们所做事情的“为什么”，评估机会规模，并将增长漏斗转化为清晰、优先级的建议。你将作为全栈数据科学家端到端地参与项目，并在数据科学、产品和增长的交叉领域建立深厚的领域专业知识。你将帮助设定增长如何衡量成功以及决定投资方向的方向，与Mercury更广泛的产品和公司目标保持一致。以下是你在工作中将做的一些事情：与增长产品、设计、工程和营销负责人合作，识别最具影响力的业务问题，并影响构建什么以及为什么。端到端拥有增长漏斗，从首次访问到注册、批准、激活和留存，构建指标、驱动树和叙事，展示我们在哪里获胜、在哪里没有。评估机会规模，帮助优先排序增长路线图和年度规划，即使在数据不完整的情况下也能给出清晰的建议。设计、分析和沟通实验（A/B测试和准实验方法），告诉团队某个变化是否有效以及下一步该做什么。根据决策调整你的工作规模：在需要时快速且方向性，在风险要求时严谨。与其他数据科学家、数据工程师和分析工程师合作，构建可信的、自助式的增长指标。向各级受众（包括高管）传达发现和建议，以清晰的观点改善企业家的生活并为Mercury创造收入。你应该具备：6年以上数据科学、产品分析或增长分析经验，包括3年以上与产品增长团队密切合作。强大的产品 sense 和塑造产品方向的记录，而不仅仅是衡量它，包括你的工作改变了团队构建内容的例子。在实验设计和分析以及漏斗、队列和留存分析方面有深厚经验。流利的SQL和统计编程语言（例如Python、R）。有产品分析和BI工具的经验，如Amplitude、Hex、Omni或类似工具；熟悉Snowflake和dbt是加分项。能够舒适地面对模糊性，并以证据为基础尊重地与高级利益相关者持不同意见。是一个清晰、简洁的沟通者，以要点为先导，并为技术和非技术受众量身定制信息。加分项：在消费者或SMB金融科技领域，或移动产品分析方面的经验。* Mercury是一家金融科技公司，不是FDIC保险银行。银行服务通过Choice Financial Group和Column N.A.提供，FDIC成员。Mercury重视多样性归属感，并自豪地成为平等就业机会雇主。所有在Mercury寻求就业的个人均不考虑种族、肤色、宗教、国籍、年龄、性别、婚姻状况、血统、身体或精神残疾、退伍军人身份、性别认同、性取向或任何其他法律保护特征。我们致力于在招聘过程中为申请人提供合理的便利。

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