Ever had your credit or debit card stolen? It happens to millions of consumers every day. That is why we built Rippleshot. We detect the data breaches that cause these problems faster than anyone else in the market. This allows banks, credit unions and merchants to act on the breaches quicker and smarter than ever before — which means consumers are alerted earlier.
Six years ago, we were the new kids on the block. Today, hundreds of banks, credit unions, and billion-dollar companies trust us to help them fight fraud. Always smarter, always faster. And we are not done. The fraudsters are good. We need to be better.
Rippleshot brings the power of machine learning and big data to help banks and credit unions reduce fraud by analyzing hundreds of millions of card transactions daily to proactively detect compromised merchants and identify an issuer’s at-risk cards.
Who We Are:
We’re an eclectic mixture of data scientists, former journalists, finance analysts and seasoned entrepreneurs who all share a passion for mission-driven work. Well…and food. We definitely love food here. But we digress.
Data breaches are more than just a headline you read in the news every day. They’re causing billion-dollar losses to merchants, financial institutions, insurers, consumers and everyone in between. At its core, Rippleshot is a tool to catch them earlier - and it’s gotten us quite a bit of attention. We’ve won innovation awards locally and globally.
That’s just the tip of the spear. We believe a tsunami of fraud is headed our way over the next five years. We think we have the winning combination of analytics, machine learning, big data, and people to protect our clients from these evolving threats. It’s a race and we are all in!
What We Need:
Rippleshot is seeking a Data Engineer in Machine Learning to help us revolutionize fraud prevention using tons of data and the most powerful tools. You’ll get to work with an experienced team of data engineers and data scientists to create unique fraud prevention models for credit unions and small banks.
This job also involves the following responsibilities:
- Analyze payment transaction data to identify patterns at the national or local level
- Leverage national consortium data set to help financial institutions, especially small ones, predict future fraud
- Analyze Terabytes of data in a cloud environment using the latest batch or streaming tools. We use Spark in AWS and look forward to trying others.
- Optimize infrastructure to enable faster feature engineering
- Build data processing architecture and systems for new data and ETL pipelines
- Recommend improvements for existing data and ETL pipelines
- Manage models and resolve data issues in production
You’ll Be a Great Fit If:
- Enjoy working with large data sets that are messy and constantly changing
- Prefer to free yourself from repetitive work by building automation with quality assurance
- Curious and drive to constantly learn and share with others
Preferred Qualifications for this role:
- 4+ years of experience as a data engineer or data research experience in a technical field
- Experience working with large data sets (Billion data points and Terabytes in size)
- Experience analyzing data to identify deliverables, gaps, and inconsistencies
- Experience with ETL processes
- Ability to communicate complex technical concepts to a broad variety of audiences
- Strong programming skills in python/pyspark
- Rigor in testing and performance optimization
- Strong experience with the Unix environment
Commitment to diversity, equity, and inclusion:
Rippleshot embraces diversity, equity, and inclusion in a serious way. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. We believe that the more diverse opinions and perspectives we have, the better our work will be and the better we can serve our customers.
- Competitive salary
- Health insurance
- Stock/Equity Options
- Flexible schedule to adapt to today’s work/life challenges
- Three weeks of vacation