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Smarsh is looking to develop state-of-the-art natural language processing (NLP) and large language model (LLM) solutions to power next-generation compliance and surveillance systems, specifically in the financial domain to uncover misconduct and risk.

Requirements

• Strong understanding of financial markets, compliance, surveillance, supervision, or regulatory technology
• Experience with one or more data science and machine/deep learning frameworks and tooling, including scikit-learn, H2O, keras, pytorch, tensorflow, pandas, numpy, carot, tidyverse
• Command of data science and statistics principles (regression, Bayes, time series, clustering, P/R, AUROC, exploratory data analysis etc…)
• Strong knowledge of key programming concepts (e.g. split-apply-combine, data structures, object-oriented programming)
• Solid statistics knowledge (hypothesis testing, ANOVA, chi-square tests, etc…)
• Knowledge of NLP transfer learning, including word embedding models (gloVe, fastText, word2vec) and transformer models (Bert, SBert, HuggingFace, and GPT-x etc.)
• Experience with natural language processing toolkits like NLTK, spaCy, Nvidia NeMo

Responsibilities

• Collect, analyze, and interpret small/large datasets to uncover meaningful insights to support the development of statistical methods / machine learning algorithms.
• Lead the design, training, and deployment of NLP and transformer-based models for financial surveillance and supervisory use cases (e.g., misconduct detection, market abuse, trade manipulation, insider communication).
• Development of machine learning models and other analytics following established workflows, while also looking for optimization and improvement opportunities
• Data annotation and quality review
• Exploratory data analysis and model fail state analysis
• Contribute to model governance, documentation, and explainability frameworks aligned with internal and regulatory AI standards.
• Client/prospect guidance in machine learning model and analytic fine-tuning/development processes

Other

• Proven collaborator, thriving on teamwork
• Excellent verbal and written skills
• Master’s or Doctor of Philosophy degree in Computer Science, Applied Math, Statistics, or a scientific field (Preferred Qualification)
• Familiarity with cloud computing platforms (AWS, GCS, Azure) (Preferred Qualification)
• Experience with automated supervision/surveillance/compliance tools (Preferred Qualification)