As an Sr. Applied Scientist, you'll work alongside domain experts, engineers, and other scientists to understand business problems, propose scientific solutions, and deploy them to production. You'll work on scientific initiatives for accelerating reconciliation, standardization, and onboarding. This includes:
- Leveraging GenAI/LLMs to build agentic solutions to accelerate accounting-related research/tasks and produce proactive insights.
- Building AI trust and safety in the financial domain.
- Establishing scalable, efficient, automated processes for large-scale data analysis, machine learning model development, model validation, and serving.
- Developing training/evaluation datasets for model fine-tuning.
You will need to have a start-up like mindset, as you will be working an in a highly iterative and collaborative environment with SDEs, Product Managers, and Accounting stakeholders to propose ideas, experiment, and scale rapidly. You should have a keen eye for what a good user experience should look like, possess excellent written and verbal communication, and have a keen interest in learning about accounting and financial processes.
- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
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