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4 meses atrás
About Credix
Credix is a FinTech company offering a Buy Now, Pay Later (BNPL) solution tailored for business-to-business (B2B) transactions in Brazil. Our platform empowers businesses to streamline their purchasing processes, improve cash flow management, and foster strong supplier relationships. As we expand in the Brazilian market, we seek a dynamic and experienced Machine Learning Engineer to join our Risk&Data team.
Why choose Credix?
- Become part of a forward-thinking start-up where boldness and a commitment to excellence are paramount, and your personal and professional development is at the forefront.
- Work alongside a dedicated team of bright individuals driven by an Olympian mindset to excel in every aspect of our operations. Together, we aim to build with velocity, utilizing innovative embedded finance strategies to expand business operations in Latin America.
- Experience a close and supportive work atmosphere where collaboration thrives, wise judgment guides our decisions, and where you have the opportunity to learn, grow, and take on meaningful responsibilities.
About the job
As we scale our presence in the Brazilian market, we are seeking an experienced Machine Learning Engineer. This person will be crucial in advancing our risk and fraud detection capabilities by developing, implementing, and deploying cutting-edge machine learning models. The ideal candidate will master both the theoretical aspects of machine learning and the practical engineering challenges, with a strong inclination towards rapid prototyping, testing, and iteration of data-driven solutions.
Qualifications
- +5 years of experience as a data scientist and machine learning engineer
- Fluent in English and Portuguese (written and verbal)
- Strong background in developing, deploying, and monitoring machine learning models in production environments
- Proven track record of applying machine learning models to assess credit risk and combat fraud in Brazil, focused on SMEs and ideally in the context of trade finance
- Experience with data pipeline and workflow management tools (e.g., Apache Airflow)
- Knowledge of cloud computing platforms (Google Cloud Platform) and their machine learning services
- Familiarity with data storage and processing technologies (e.g., SQL, NoSQL, Hadoop, Spark)
- Ability to work in a fast-paced, entrepreneurial environment, adapting to changing priorities and managing multiple projects simultaneously.
- Analytical mindset, combined with outstanding interpersonal and communication skills
Responsibilities
- Design, implement, and deploy machine learning models to detect fraud and assess risk in real-time
- Develop scalable data pipelines to ingest, clean, and process diverse datasets from various sources
- Collaborate with the product and engineering teams to integrate machine learning models into the core product
- Monitor and maintain the performance of machine learning models in production, making adjustments as necessary to ensure optimal operation
- Conduct experiments, perform back tests and prototype new models to improve CrediPay's risk and fraud detection capabilities continuously
- Unblock critical paths in the core product to enhance overall velocity and efficiency
- Stay up to speed with the latest advancements in machine learning and data engineering, incorporating new technologies and methodologies to drive innovation
Join our dynamic and ambitious team:
With access to our offices in Sao Paulo or Antwerp, you'll immerse yourself in a culture of innovation and collaboration. But that's just the beginning - here's what else we offer:
- A culture of learning and experimentation: where you are encouraged to explore new ideas and technologies
- Competitive salary package : Your hard work deserves recognition, and we ensure you're well-rewarded for your contributions.
- Equity stock options plan : Be a part of our journey towards success and share in the rewards.
- Paid holidays : Enjoy the flexibility to recharge and rejuvenate
- Bi-annual off-sites : Awesome team building, unforgettable memories, and adventures ensured during our team off-sites.
Please submit your application along with an English version of your resume.
