We are currently hiring for a mixed role that
is 70/30% ML Ops/Risk Data Scientist. Join us, and you will contribute to
building our decision and risk engine.
- Oversee and deploy ML
pipelines, from development to production.
- Administer CI/CD pipelines, ensuring
tests succeed and artifacts are properly stored.
- Monitor model performance
metrics and set up alert systems for anomalies.
- Develop credit, fraud scoring
and other predictive models.
- Engage with stakeholders to
understand requirements and manage expectations.
- Document processes, and share
knowledge and expertise.
- Manage project planning,
execution, and progress tracking.
Requirements
- Bachelor’s or Master’s degree
in Computer Science, Engineering, or a related field.
- Proficiency in Python, SQL,
and database management.
- Experience with Docker and
deploying applications on cloud platforms such as AWS.
- Strong experience with CI/CD
pipelines, automated testing, and deployment.
- Ability to use effectively monitoring tools and establish responsive alert systems.
- Robust documentation skills to
clearly record processes and optimizations.
- Familiarity with SageMaker and
other AWS services would be a significant advantage.
Benefits
- Competitive compensation.
- An agile culture with a flat
hierarchy, offering the opportunity to tackle complex, real-world
challenges.
- A team comprised of top-tier
professionals with experience in leading consultancies and banks.
- The chance to play a pivotal
role in building a data-driven culture.