Flowcast powers smarter credit decisions with AI to unlock credit for the underserved SME market. 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.

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.

What you'll be doing at Flowcast:

• Designing and implementing highly-scalable data pipelines for real-time prediction, streaming and batch processing

• Working with a large amount of structured and unstructured datasets

• 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

• Highlighting and reviewing data anomalies and quality issues

• Performing explorative analysis on external and public data sources to pull them into their ecosystem

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

Things that make you a good fit:

• Professional data 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 data workflow systems such as Apache Airflow

• Demonstrated knowledge of distributed computing frameworks such as Apache Spark

• Demonstrated exposure to the Python machine learning ecosystem, including Pandas, Flask and ETL

Appreciated but not required:

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

• Knowledge of Business Intelligence Tools such as Tableau

• Experience in the fintech industry a plus

• Proven track record of collaboration with a variety of decision-makers to drive impact

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