Machine Learning/Data Engineer
In 2017, immigrants worldwide sent over $600 billion home to family and friends, dwarfing foreign governmental aid. In the age of cheap, quick transfers through services like Paypal and Venmo, these people are trekking to stores to pay fees averaging over 7% for transfers that typically take 24 hours or more.
Wave's mission is to change that by making sending money anywhere in the world easy and affordable. Since 2014, our app has allowed Africans in the US, the UK, and Canada to send money instantly to mobile money wallets in Kenya, Uganda, & Tanzania, saving our users over 70% relative to Western Union and MoneyGram.
As we serve more and more customers, Wave will need to ensure that we're able to identify and prevent fraud. We're building a world class risk team comprised of engineers, data scientists, and operations team members to ensure that we're able to fight fraud and provide our customers with the best user experience possible.
We've gotten pretty good at fighting fraud but we are looking to rapidly expand throughout Africa in the next year, including Nigeria. That's where you come in...
How you'll help us achieve itOur Risk Engineers build and support everything that helps lower our risk exposure. This includes integration of vendor tools, training new fraud/anomaly detection algorithms, supporting our risk operations teams and improving our monitoring capabilities. You'll leverage your technical skills to build out new tooling for our Risk Representatives, integrate new data sources, build feature engineering pipelines and help evaluate new models.
On any given day, you may:
- Evaluate and integrate a new data sources for our fraud detection algorithm in real-time and pipe the results into our analytical environment.
- Update a defense heuristic when we discover a new fraud ring/pattern.
- Automate the training and deployment of updated models.
- Develop a new anomaly detection algorithm, helping us spot strange behavior before a chargeback occurs.
- Create real-time and batch ETL jobs for our analytical tools.
- Integrate vendor data and models into our risk defenses.
- Enhance our Risk Representatives' tooling to better capture their judgements in order to improve our models and risk flags.
- Help build experiments to evaluate new models, third-party data sources and tooling.
- Modifying UIs that serve alerts to our Risk Representatives to help them work fraud alerts more efficiently.
We're building out a best-in-class Risk infrastructure that can enable us to grow and we're hoping to find an engineer that's excited to solve data-driven problems in an adversarial environment.
- Location: Our company is 100% remote. You can be based anywhere in the United States, Canada, United Kingdom, Italy, Belgium or Germany
- Length of position: Permanent.
- 1-4 years professional experience as a software engineer building data driven systems.
- Work authorization in the country in which you intend to be based.
- Experience in one or more of the following areas:
- Cloud (AWS or GCP)
- Data Engineering (Kafka, Spark, Flink or similar tools)
- Machine Learning (Scikit Learn, Tensorflow, Keras, XGBoost etc...)
You might be a good fit if you
- You like building data driven systems and know databases and SQL.
- Demonstrate tenacity and a willingness to go the distance to get something done. You don't mind doing things manually but automate at every opportunity.
- Are inquisitive, intellectually curious and can make sense of complex systems or information.
- Can work in a structured approach towards goals and pay attention to detail.
- Can easily communicate with non-technical folks and translate their feedback into code.
- Are comfortable defaulting to over-communication and overreaching when it comes to coordination.
- Adjust quickly to changing priorities and conditions and cope effectively with complexity and change.
Bonus points if you
- Possess hands-on experience with Python
- Operationalized ML models or have a strong interest in ML
- Have fraud or financial experience.