Strike allows users to send and receive money anywhere, instantly, with no added fees. Strike is built on top of the Bitcoin network – the largest global, interoperable and open payments standard. Strike believes that open payment networks enable universal participation in the financial system, ushering in a new digital economy with truly borderless money transfers. Strike leverages Bitcoin’s open payment network to offer users the first global peer-to-peer payments app and a novel bitcoin-native payments experience.
We are looking for a seasoned data scientist who lives and breathes data. Must have demonstrated experience using machine learning to solve complex problems. Our most immediate need is mitigating fraud risk while continuing to provide a world-class user experience to our customers. This is a high-impact role working to define the systems, tools, and analytics which will define our company trajectory.
- Design machine learning and optimization systems to power risk reduction and revenue growth efforts across the company
- Discover, catalog, and integrate disparate data signals such as in-app behavioral data, market data, and external risk scores
- Work with engineers to deploy production-grade models into our existing systems
- Build model monitoring and alerting systems to measure model quality against expectations and fix problems as they arise
- Collaborate with the rest of the data team and partner with product, marketing, finance, and customer service teams to deliver data products and solutions
- 5+ years of professional experience as a data scientist, machine learning engineer, or similar role
- Demonstrated experience building and deploying large scale models in industry with a strong track record of success
- Financial industry experience strongly preferred
- Experience with supervised and unsupervised learning algorithms on tabular financial data, including highly imbalanced datasets. Familiarity with time series analysis and forecasting
- Excellent Python programming skills and knowledge of machine learning libraries such as pandas, scikit-learn, and pytorch
- Experience with BI tools such as Tableau, Looker, or Mode
- Deep problem solving abilities, detail orientation, resourcefulness
- Familiarity with banking processes and fraud domains such ACH fraud, card fraud, first-party fraud, account takeover, identity theft and synthetic identity fraud.
- Experience working with cloud ML systems such as Amazon Sagemaker or Kubeflow
- Salary range: $165 - $180K. The actual salary will be determined using location and experience based data.
- Equity in a high growth startup
- Health, dental, and vision insurance premium contributions; short & long term disability insurance and basic life insurance
- Cell phone and internet reimbursement
- Flexible PTO, sick leave & parental leave
- Access to participate in a company 401k plan
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