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Be a part of the company that’s transforming the way merchants do business

We’re a team of inspired problem solvers building powerful, intuitive point-of-sale tools for small and medium businesses. Hardware that’s stylish and functional. Software that scales to any business. We’ve sold over one million Clover devices to restaurants and shops all over the world—and probably in your own neighborhood.

Important Information on Clover’s COVID-19 Vaccination Policy

In order to protect our Clover community, Clover requires all newly hired employees in the United States to be fully vaccinated before their start date. Proof of vaccination will be a condition to hiring. Clover complies with all applicable laws regarding the reasonable accommodation of individuals with disabilities and/or sincerely held religious beliefs.

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Data Analyst

Job ID R-10250211 Date posted 12/02/2021

About Clover

Join the Fintech and SaaS revolution that is helping small businesses grow faster and get the technology and
insights previously available only to the “big guys.” At Clover, you will be part of an entrepreneurial team working
in a fast-paced and high growth environment, with the benefits of a parent company, First Data, that is the
largest payments processor and merchant acquirer worldwide.

Clover delivers the leading Point of Sale system with an elegant end-to-end solution that incorporates beautiful
devices, cloud-based POS software, payments processing, platform API’s for third-party developers, and an
ecosystem with over 500 apps. The Clover platform delivers solutions in a scalable and modular fashion that
powers tiny merchants through large football stadiums, supporting millions of transactions daily.

About the role

The data team is looking for a data analyst who is interested in leading the effort to promote a data-driven culture
at Clover. We are looking for a detail-oriented problem solver who has a track record of strategically building
data visualization tools and conducting in-depth analyses for an executive audience. In addition, our ideal
candidate will be passionate about systematically finding creative approaches to most effectively bridge the gap
between data and decision makers.

Responsibilities

- Serve as a bridge between decision-makers and the data needed to provide actionable insights
- Collaborate with product, engineering, and operational teams to proactively gather requirements, develop
metrics, instrument features, and deploy monitoring tools to measure product performance
- Design, develop, own, and maintain ETL/ELT data flows across a constellation of data sources and systems
- Use Python libraries (matplotib, seaborn) and business intelligence tools (Tableau, Sigma) to create data
visualizations that enable teams to make data-driven decisions quickly and easily
- Conduct in-depth analyses using statistical techniques to uncover insights into the factors that drive product
performance
- Lead efforts to improve data literacy and fluency within the company by contributing to data warehouse
development, technical documentation, and training teams to become self-sufficient in data exploration and
analysis

Requirements

- Extensive industry experience providing analytical services to executive-level stakeholders of varying technical
backgrounds
- Hands-on experience building and iterating on self-serve, data visualization tools
- Working knowledge or experience in data collection, transformation, and storage from various sources
including relational databases, APIs, logs, and unstructured files
- Strong self-starter who can create and lead new projects from scratch with minimal guidance, oversight, or
resources
- Collaborative, team player who can thrive in a fast-paced environment with quickly changing, ambiguous
requirements
- Proficiency in Python (or at least one scripting language) and SQL

Nice to have

- Masters degree in a quantitative discipline: computer science, applied mathematics, statistics, operations
research, information systems, engineering, economics, social sciences, or equivalent
- Experience implementing one or more or of the following techniques: A/B testing, regression analysis, non-
parametric models, time series analysis, experimental design, survival analysis, clustering, natural language
processing, or neural networks
- Industry experience developing cloud-based data warehouse solutions
- Experience with technologies such as Kafka, Docker, Kubernetes, and Spark

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