In 1923, Claude Hopkins published Scientific Advertising which many say has the first descriptions of A/B testing and using coupons for attribution. Behind the book was a team that meticulously designed, executed, and measured these campaigns that helped set the standard for direct marketing that is used to this day. 

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 the Revenue org, as well as Marketing and the Growth team to inform how we invest in and build our new user experience to grow the number of businesses on Mercury. 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 our Sales funnel to attract new users and introduce existing customers to new products.
• Partner with Revenue team 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.
• 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 on Sales cycles.
• Identify opportunities for ML to improve the Sales experience.

You should:

• Have 5+ 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 product teams.
• Experience in revenue analytics (sales funnel conversions, user segmentation, GTM experience) 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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