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As an 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:
- Developing ML models or leveraging foundation models to produce proactive insights.
- Leveraging LLMs to answer questions asked during month-close reconciliation research.
- Run experiments over large datasets for new predictive learning approaches for financial analysis.
- Establish 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 solving business problems through machine learning, data mining and statistical algorithms experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- 3+ years of programming in Java, C++, Python or related language experience
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience using Unix/Linux
- Experience in professional software development
- Experience with finance or accounting
- Experience with TypeScript and React
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