Flowcast powers smarter credit decisions with AI to unlock credit for the underserved SME market. This is an outstanding opportunity to join a rapidly growing, series A, remote-friendly fintech with a dynamic team. You will be building the future of trade finance and SME lending using cutting-edge AI/ML techniques.

Flowcast leverages its patented machine learning methodologies to create high-performing predictive models that assess risk and enable lenders to unlock credit to all size businesses at scale. Flowcast’s models offer unparalleled explainability in understanding the root cause of a business’ creditworthiness, the risk of delinquency, the timeliness of repayment and the likelihood of dilution of their transactions. Unlike traditional risk assessments, the API-based solution updates and monitors the risk assessment of the borrower in real-time based on alternative data and Flowcast’s proprietary data.

Flowcast is seeking an ambitious and self-motivated Senior Machine Learning Engineer to join their core engineering team to keep up with customer demand. Their ideal teammate is an experienced professional looking to take ownership of critical components of Flowcast's proprietary machine learning platform.

What you'll be doing at Flowcast:

• Designing and coding highly-scalable machine learning models for real-time prediction, streaming and batch processing

• Working alongside other engineers, data scientists, and product teams to build AI decision engines for financial institutions and Fortune 500 corporations

• Enabling data scientists to iterate quickly and deploy machine learning models into production

• Providing guidance in developing highly scalable algorithms based on state-of-the-art machine learning and deep learning methodologies

• Following agile processes with a focus on delivering production-ready testable code in small iterations

Things that make you a good fit:

• Able to work autonomously and resourcefully in a fast-paced startup environment

• 4+ years of professional machine learning engineering experience in data-intensive environments

• Familiarity with concepts in data mining, data engineering, and backend API development

• Advanced skills and experience using Python, SQL, Bash and other scripting/compiled languages

• Demonstrated knowledge of distributed computing frameworks such as Apache Spark

• Demonstrated experience with the Python data engineering & machine learning ecosystem, including Pandas, Flask and ETL

• Experience with or exposure to Amazon Web Services (AWS), Google Cloud Platform (GCP), Git, Jenkins and Docker-based continuous-integration & deployment pipelines

Appreciated but not required:

• Experience in the fintech industry

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