Requirements

Proven track record of deploying models from scratch including data preprocessing, feature engineering, model selection, evaluation, and monitoring
Solid knowledge of Python with ability to make and justify design decisions in code
Experience using Git for collaboration, including code reviews and Pull Requests
Ability to read through code, especially Java, and collaborate with engineering teams
Experience working with large datasets and data processing technologies such as Hadoop, Spark, and SQL
Familiarity with anomaly detection, supervised and unsupervised learning methods, and real-time data analysis
Strong statistical analysis skills and good presentation abilities to translate insights into action
Strong product mindset with ability to work independently across cross-functional teams
Good communication skills with ability to convey ideas to non-technical stakeholders
Strong problem-solving skills including refining problem statements and figuring out solutions

Nice to Haves

Experience with MLOps tools such as Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, and CI/CD pipelines
Prior experience in the fraud domain and strong understanding of fraud detection techniques
Experience designing and deploying large language model (LLM) based solutions in production

Leading the development and deployment of machine learning models to improve Receive product performance across multiple markets
Analyzing large volumes of customer, transaction, and product data to identify trends, risks, and opportunities
Designing and implementing experiments to evaluate product changes, decisioning systems, and customer experience improvements
Building scalable modeling approaches for prioritization, personalization, risk management, and operational decision-making
Collaborating with analysts, product managers, engineers, operations, and risk teams to translate business needs into data science solutions
Developing robust data pipelines, algorithms, and tools to support production-grade modeling and decision-making
Staying up to date with the latest advancements in data science, machine learning, and payment fraud prevention techniques