资深数据科学家 - 风险机器学习
Staff Data Scientist - Risk ML
1999年，NASA在火星气候轨道器从地球出发9个月后与其失去联系。它开始执行预定的轨道插入操作，但在飞过火星背面后失去了无线电联系。虽然我们可能永远无法知道它是否在大气中被摧毁或重新进入太阳系空间，但我们可以得出结论：在追求星辰大海时，把细节（比如单位）弄对至关重要。
尽管冥王星的宇宙旅程可能是比喻性的，但我们有自己的高远抱负，并需要将这些抱负与精确的数据分析结合起来。
为此，我们正在招聘一名专注于机器学习的数据科学家，以支持我们的风控团队。该团队不仅负责检测、监控和缓解第一方和第三方欺诈，还要确保我们了解并理解客户，同时监控他们的行为以识别金融犯罪风险。你将在加强我们的欺诈防御方面发挥关键作用，同时确保冥王星继续提供流畅且值得信赖的银行*体验。
这是一个加入冥王星的关键时刻的机会。你将参与解决业务中最关键的挑战，并与产品、工程和风控团队合作，保护我们的客户和整个金融系统。
以下是你会从事的工作：
- 构建、验证和部署机器学习模型，以实时识别和防止欺诈
- 通过文档、测试和监控来支持这些模型的可重复性和稳健性
- 确保数据管道和工具中的数据质量和可靠性
- 与风控策略团队合作，构思模型输入和应用，并与工程团队优化部署和可观测性
- 担任技术负责人，进行原型设计、迭代和制定最佳实践，并带领团队共同进步
你需要具备：
- 7年以上处理和分析大型数据集以解决问题并产生影响的经验，其中5年以上机器学习经验
- 熟练使用SQL，并有使用它来理解和管理不完美数据的经验
- 熟练掌握Python，并有统计建模和机器学习的经验
- 有在生产环境中部署和监控机器学习模型的经验
- 能够在快节奏、优先级不断变化的环境中工作
- 有领导和赋能他人的能力，不仅完成自己的工作，还能提升周围人的水平
- 具备...
查看英文原文
In 1999 NASA lost contact with its Mars Climate Orbiter after a 9 month journey from Earth. It began its planned orbital insertion maneuver but went out of radio contact after passing behind Mars. While we may never know whether it was destroyed in the atmosphere or re-entered heliocentric space, we can draw the lesson that getting the details (in this case, units) right is critical, especially when shooting for the stars.
While Mercury’s cosmic journey may be more metaphorical, we have our own sky-high ambitions and the need to marry those with precise data analysis.
To that end, we are hiring a Machine Learning-focused Data Scientist to support our Risk team. This team is responsible not only for detecting, monitoring, and mitigating both first- and third-party fraud but also ensuring we know and understand our customers while monitoring their behavior for financial crime risk. You’ll play a key role in strengthening our fraud defenses while ensuring that Mercury continues to deliver a smooth and trustworthy banking* experience.
This is an opportunity to join Mercury at a pivotal moment in our growth. You’ll be working on some of the most critical challenges facing the business and collaborating across product, engineering, and risk to protect our customers and the financial system at large.
Here are some things you’ll do on the job:
- Build, validate, and deploy machine learning models to identify and prevent fraud in real time
- Support the reproducibility and robustness of said models through documentation, testing, and monitoring
- Ensure data quality and reliability across pipelines and tools
- Collaborate with Risk Strategy to ideate on model inputs and applications and with Engineering optimize deployment and observability
- Act as a technical lead prototyping, iterating on, and codifying best practices - and bringing the rest of the team along
You should have:
- 7+ years of experience working with and analyzing large datasets to solve problems and drive impact, with 5+ years of ML experience
- Proficiency in SQL and experience using it to understand and manage imperfect data
- Proficiency in Python and experience with statistical modeling and machine learning
- Experience deploying and monitoring machine learning models in production
- Comfort working in a fast-paced environment with evolving priorities
- Demonstrated ability to lead and empower others, delivering not just on your own work, but upleveling those around you
- The ability to drive strategic alignment between teams with differing roadmaps, timelines, or architectures
Ideally you also have:
- 1+ years of relevant risk experience
- Familiarity with LLMs or other GenAI and how they can be applied to risk or fraud detection
- Experience with modern data tools for pipelines and ETL (e.g., dbt)
- Experience with model governance as required in finance or other regulated industries
- Experience building zero-to-one solutions in ambiguous or greenfield problem spaces
*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 applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.
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Total Rewards
The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits.
Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.
Our target new hire base salary ranges for this role are the following:
US employees (any location):
$239,000—$298,800 USD
Canadian employees (any location):
$225,900—$282,400 CAD