SecurityScorecard is a continuous, non-intrusive risk benchmarking enterprise platform. At SecurityScorecard, we are building a cloud-based threat intelligence solution and big-data security platform, pushing the limits of technology, and energizing our growth while at the same time improving the data accuracy and reliability of our services.
We are looking for an accomplished Data Scientist with practical commercial experience to join our company and our Data Science teams. SecurityScorecard is a SaaS that’s disrupting cloud security space, we are well funded and raised a 12.5m Series A from Sequoia Capital – https://www.sequoiacap.com/. We already have a top notch team in the middle of NYC in a hot new office.
We are working on some very interesting problems with large data sets, machine learning, security, and visualization.
You’re a seasoned Data Scientist who loves difficult data problems and enjoy cyber security challenges. You’ve built and deployed commercial solutions for a variety of purposes. You love open source and contribute to some. You have experience with startups that grow at a blistering pace. You experiment with technologies and can easily prototype new ideas.
You will be successful in this role if you:
- Have experience developing predictive models and classifiers which have been implemented in commercial solutions
- Are not afraid to fail fast and move on to the next idea until the problem is solved
- Are extremely clear, concise and effective in both written and verbal communications
- Can collaborate with engineers across different teams to understand and solve problems in high pressure situations
- Love having an impact and enjoy participating in a successful growth organization
- Commercial experience developing predictive models or classifiers
- Programming experience in Python and open-source numerical and scientific libraries
- Experience with Scikit
- Experience working in a fast-paced and collaborative environment
- Cyber security experience a big plus
- Understanding of Agile development practices
- Experience with ETL techniques
- Knowledge and experience of NLP concepts a plus.
- Gather and process raw data at scale (including writing scripts, calling APIs, writing SQL queries, etc.)
- Create a variety of predictive models from our datasets that can support security research.
- Visualize a variety of threat signal data to uncover how security vulnerabilities and malware spread across networks.
- Support business decisions with ad hoc analysis as needed.
- We use AWS extensively, so experience with the Amazon tech stack will help you hit the ground running.