AI Research Scientist

It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world’s relationship with money to make it more relatable, instantly available, and universally accessible.

Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We’ve been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy.

The Role

This role is part of our Cash App R&D team which sits within our Machine Learning and Data Science organization. This team’s mission is aimed at developing advanced deep learning based signals and learned representations for Risk ML models. The team has the mandate to explore, develop and implement alternatives to traditional feature based ML with SOTA and to communicate that work at public venues (e.g. publishing).

You Will

• Lead the conception, design, and implementation of large-scale experiments to validate novel ideas rapidly and comprehensively.
• Apply the latest theoretical advancements to enhance existing products, processes, and technologies, ensuring they remain at the forefront of the industry.
• Engage in the creation of experiments, prototyping, and architectural design, contributing to a diverse range of computer science domains such as machine learning, data mining, natural language processing, and performance analysis.
• Author and publish research papers that communicate findings to both the team and the broader research community, establishing a significant impact on the field.
• Contribute to the growth of research initiatives by sharing emerging trends and best practices, fostering a collaborative and innovative environment.
• Take the lead in defining data structures, frameworks, designs, and evaluation metrics for research solution development, exhibiting autonomy in decision-making.
• Collaborate with internal and external partners, identifying potential research areas and participating in the development of long-term research strategies.
• Conduct experiments aligned with research inquiries, employing simulations and prototypes to assess the outcomes of investigations.
• Refine research methodologies, solidifying hypotheses, and contributing to the evolution of research practices under minimal supervision.

You Have

• A Ph.D. degree in a quantitative field (computer science, physics, math, etc) with significant experience using and developing machine learning and AI methodologies.
• Proven experience in conducting research projects with tangible outcomes.
• A demonstrated history of generating intellectual property within the field. These contributions could take the form of open source software, patents, or refereed publications in venues such as conferences, journals, books, etc.
• Proficiency in machine learning techniques, data mining, experimental design, and academic publishing.
• Strong programming skills in languages such as Python, TensorFlow, or PyTorch.
• Excellent problem-solving abilities and a creative mindset to tackle complex challenges.
• Effective communication skills to collaborate with cross-functional teams and contribute to the research community.

Technologies We Use and Teach

• Python
• NumPy, Pandas, sklearn, xgboost, 
• TensorFlow, Pytorch, keras, etc
• MySQL, Snowflake, 
• GCP/AWS and 
• Spark