In the mid-1950s, William Fair and Earl Isaac teamed up to develop a groundbreaking approach to credit risk assessment. Drawing on their expertise in economics and mathematics, they sought to address the challenge of evaluating borrowers' creditworthiness more accurately and efficiently. Through meticulous data analysis and modeling, Fair and Isaac pioneered the concept of credit scoring, introducing statistical techniques to predict the likelihood of loan default based on historical data. Their innovative Fair Isaac Credit Scoring System, launched in 1956, revolutionized lending practices by providing lenders with a quantitative tool to assess risk objectively. This development not only improved access to credit for deserving individuals but also laid the foundation for modern credit scoring methodologies that continue to shape financial decision-making today.

At Mercury, we’re looking to seed our team with candidates who can use their background in analytics applied with product development to accelerate the adoption of Mercury. In this role, you’ll be responsible for proactively deriving data insights and partnering with engineering, marketing, design, onboarding, and other product business teams to inform how we invest in and build our Credit Lending product. 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 develop various skills as a full-stack Data Scientist working on projects end-to-end and build deep domain expertise in the intersection of Data Science and Product..

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

  • 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.
  • Analyze historical data to identify trends, patterns, and risk factors, informing the design of risk mitigation strategies and credit policy adjustments.
  • Partner with Ecommerce Lending Product 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.
  • Serve as the Data Lead for the current lending product and provide thought leadership & strategic thinking to influence engineering, design, and business teams and implement data-based recommendations to build toward future lending products
  • Work with the cross-functional team to identify underwriting improvement opportunities, monitor customer portfolios, evaluate portfolio risk and make appropriate recommendations for scaling and efficiency 
  • Leverage data models and advanced analytics techniques to design long-term solutions including enhancements of existing strategies and building new process improvements 
  • Develop and execute data-driven experiments and simulations to evaluate the performance of loan products under different eligibility criteria and loan terms

You should:

  • Have 7+ years of experience working with and analyzing large datasets to solve problems and drive impact 
  • Have fluency in SQL, and other statistical programming languages (e.g. Python, R, etc.).
  • Have experience building scalable data pipelines and ETL processes with DBT and understand different database structures.
  • 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 familiar with analytical models/analysis used to support loan underwriting and account management underwriting policies
  • Experience in credit risk analytics (model development, strategy and framework, scorecard development, documentation, validation, governance, implementation and automation etc.) will be a strong advantage

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): $203,100 - 238,900 USD
  • Canadian employees (any location): CAD 184,800 - 217,400

Mercury is a financial technology company, not a bank. Banking services provided by Choice Financial Group and Evolve Bank & Trust, Members FDIC.

We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on January 22, 2024. Please see the independent bias audit report covering our use of Covey here.

 

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