Required Software Engineer, ML (Technical Leadership)
The financial risk management (FRM) machine learning principal will be the most senior machine learning engineer and strategist for financial risk. The principal will enable the risk organization to deliver significant lift over current long range objectives for friction and leakage through the generation of new Machine Learning opportunities for the organization and support of successful delivery of the risk management ML architecture. This person will partner closely with the FRM engineering leader (Director level) and be part of our risk management leadership circle.
Software Engineer, ML (Technical Leadership) Responsibilities
Address core business and technical machine learning opportunities: elevate the existing portfolio of machine learning solutions to be state-of-the-art for minimizing our financial losses (due to leakage, good revenues loss and friction). Following are a few examples of technical and business problems we aim to address. – Provide a solution for optimizing the risk machine learning model ensemble (covering the entire end-to-end advertiser funnel including detection, decisioning, enforcement and remediation) through optimization of the current model portfolio and individual models. – Minimize the impact of the prolonged financial fraud feedback loop. – Improve models measurement and performance. – Optimize data/label strategy. – Optimize balance between specific targeted model strategy and broad umbrella model strategy to optimize for short and long term benefits
Lead Research and Introduction of Advanced Technologies: – Collaborate with Financial Integrity's senior ML Engineers to lead the research and introduction of deep learning and Large Language Model (LLM) technologies. – Remain current on industry-wide advancements in ML and introduce relevant advancements in Financial Risk Management
Collaborate on Next-Generation ML Architecture: – Work closely with financial harms principals and risk management tech leads to deliver the next-generation ML architecture for our risk management system. – Collaborate with Principal ML engineers from across the company to adopt best industry and our practices within the FRM team. – Resolve or mitigate design dilemmas, balancing business and technical trade-offs. – Identify and initiate opportunities for collaboration and impact with other organizations
Identify and Initiate New Business Opportunities: – Collaborate with our FinTech, Central Integrity and Core Ads Growth partnerships to identify and initiate new business opportunities based on third-party capabilities. – Conduct proof of concept for different opportunities and initiate integrations to enhance business performance
Grow Other Senior ML Engineers – Actively invest in the growth of other senior ML engineers through goal-driven formal and informal mentorship. Provide regular feedback to other engineers regarding their technical work.
The financial risk management (FRM) machine learning principal will be the most senior machine learning engineer and strategist for financial risk. The principal will enable the risk organization to deliver significant lift over current long range objectives for friction and leakage through the generation of new Machine Learning opportunities for the organization and support of successful delivery of the risk management ML architecture. This person will partner closely with the FRM engineering leader (Director level) and be part of our risk management leadership circle.
Software Engineer, ML (Technical Leadership) Responsibilities
Address core business and technical machine learning opportunities: elevate the existing portfolio of machine learning solutions to be state-of-the-art for minimizing our financial losses (due to leakage, good revenues loss and friction). Following are a few examples of technical and business problems we aim to address. – Provide a solution for optimizing the risk machine learning model ensemble (covering the entire end-to-end advertiser funnel including detection, decisioning, enforcement and remediation) through optimization of the current model portfolio and individual models. – Minimize the impact of the prolonged financial fraud feedback loop. – Improve models measurement and performance. – Optimize data/label strategy. – Optimize balance between specific targeted model strategy and broad umbrella model strategy to optimize for short and long term benefits
Lead Research and Introduction of Advanced Technologies: – Collaborate with Financial Integrity's senior ML Engineers to lead the research and introduction of deep learning and Large Language Model (LLM) technologies. – Remain current on industry-wide advancements in ML and introduce relevant advancements in Financial Risk Management
Collaborate on Next-Generation ML Architecture: – Work closely with financial harms principals and risk management tech leads to deliver the next-generation ML architecture for our risk management system. – Collaborate with Principal ML engineers from across the company to adopt best industry and our practices within the FRM team. – Resolve or mitigate design dilemmas, balancing business and technical trade-offs. – Identify and initiate opportunities for collaboration and impact with other organizations
Identify and Initiate New Business Opportunities: – Collaborate with our FinTech, Central Integrity and Core Ads Growth partnerships to identify and initiate new business opportunities based on third-party capabilities. – Conduct proof of concept for different opportunities and initiate integrations to enhance business performance
Grow Other Senior ML Engineers – Actively invest in the growth of other senior ML engineers through goal-driven formal and informal mentorship. Provide regular feedback to other engineers regarding their technical work.
Requirements:
Minimum Qualifications
Extensive experience in supporting and evolving a portfolio of ML models that deliver on critical business goals
Preferred Qualifications
Experience working with ML models in financial risk or similar financial contexts.
Minimum Qualifications
Extensive experience in supporting and evolving a portfolio of ML models that deliver on critical business goals
Preferred Qualifications
Experience working with ML models in financial risk or similar financial contexts.
This position is open to all candidates.
