Senior Data Scientist - Risk Analytics

San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or United States

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Mercury was founded to make banking better for all businesses. We started by imagining what the best banking platform for startups would look like and in a few years we have thousands of customers using Mercury’s products, supporting their businesses as they grow.

As Mercury continues its rapid growth trajectory, navigating the complex and ever-evolving partnerships and regulatory landscape is not just a necessity but a competitive advantage.

Our mission is to transform how banking operates, and we have a staggering amount of work ahead. This means you will have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

Mercury is building a complete finance stack for startups. We work hard to create the easiest and safest banking experience possible to simplify entrepreneurs' and business owners’ financial lives.

To enable this stellar banking experience, we need to have a deep understanding of our customers from the moment they onboard and mature it each time they interact with our product.

We are looking to hire a data scientist who will advance our data-first approach to managing risk by surfacing key metrics, building automated and reliable reporting to cross-functional teams, diving into complex data questions, and working to enhance our data infrastructure and understanding from end-to-end.

In this role, you’ll be responsible for proactively deriving data insights and partnering with risk, compliance, engineering, product, and design to inform how we understand and mitigate financial, regulatory, and reputational risk.

You’ll build a data-informed culture across Mercury so that we can all determine what’s happening, react quickly, and invest intelligently.

You will work on projects end-to-end and build deep domain expertise in the intersection of Data Science and Risk.

Here are some things you’ll do on the job :

• Own and manage core risk business and operational metrics and data, becoming the company-wide expert on the topic.
• Build observability for our internal operations and risk controls efficacy through analytical deep dives and key metric dashboards.
• Respond to ad hoc data requests from within Risk and from cross-functional partners in Audit, Compliance, and Product.
• Partner with Risk stakeholders and cross-functional teams to identify impactful business questions, conduct deep-dive analysis, translate data insights into actionable recommendations and communicate findings to audiences at all levels to inform data-driven decisions.
• Analyze historical data to identify trends, patterns, and risk factors, informing the design of risk mitigation strategies.
• Collaborate with other Data Scientists and Data Engineers to build and improve data pipelines, tools, and infrastructure to streamline data collection, processing, and analysis workflows, and ensure the integrity, reliability, and security of data assets.
• Leverage data models and advanced analytics techniques to design long-term solutions including enhancements of existing strategies and building new process improvements.

You should :

• Have 4+ years of experience working with and analyzing large datasets to solve problems and drive impact, with 2+ years of experience working in risk.
• Have fluency in SQL and experience using it creatively with imperfect data.
• Have consistent experience with developing dashboards using data visualization tools.
• Have the ability to proactively ask questions, turn them into analyses, and make your case to various stakeholders, including senior leadership.
• Be super organized and communicative. You will need to prioritize and manage projects to maximize impact, supporting multiple stakeholders with varying quantitative skill levels.
• Be comfortable with decision making in a fast-paced, ambiguous environment, while demonstrating curiosity and a growth mindset.

Ideally you also :

• Have fluency in additional statistical programming languages (e.g. Python, R, etc.).
• Have experience building scalable data pipelines and ETL processes with DBT and understand different database structures.

The total rewards package at Mercury includes base salary, equity (stock options), 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) : $173,600 - 204,200 USD
• Canadian employees (any location) : CAD 158,000 - 185,800

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