Description:
• Design, develop, and deploy machine learning models to detect and prevent financial crimes such as money laundering, human exploitation, and terrorist financing.
• Collaborate with stakeholders, business partners, and product engineering teams to build effective solutions within the regulatory program.
• Use data to support efficient, scalable solutions that protect users and reduce bad activity on the platform.
• Produce and maintain documentation that can withstand regulatory scrutiny.
• Proactively identify new opportunities and future needs for the ML team.
• Lead by example by applying machine learning and engineering best practices.
• Stay current on machine learning developments and apply new learnings where appropriate.
Requirements:
• 6+ years of machine learning experience with a focus on modeling and deployment.
• A degree, preferably graduate level, in Computer Science, Engineering, Statistics, Physics, Applied Math, or a related technical field.
• Expertise in at least one of the following: Natural Language Processing, Graph Neural Networks, Reinforcement Learning, or model explainability.
• Prior experience partnering with product, engineering, and business teams to prioritize, scope, design, and deploy ML tooling and infrastructure at scale.
• Ability to clearly communicate technical and non-technical concepts to cross-functional, globally distributed teams.
• Natural curiosity and desire to grow and help shape all aspects of the team.
• Experience with Python and related libraries such as NumPy, Pandas, Scikit-learn, and PyTorch, with occasional Kotlin and Java.
• Experience with Snowflake, GCP, AWS, and orchestration tools such as Prefect and Airflow.
• Experience with transformer models such as BERT and LLMs.
Benefits:
• Work from anywhere in the United States or Canada.
• Starting pay of $228,700 to $343,100 USD in Zone A, $217,300 to $325,900 USD in Zone B, $205,900 to $308,900 USD in Zone C, or $194,500 to $291,700 USD in Zone D.
• Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning benefits.
• Reasonable accommodations provided for disabled applicants during the recruitment process.
• Inclusive interview experience and equal opportunity hiring practices.
• Consideration of qualified applicants with arrest or conviction records under applicable fair chance laws.