Role overview
- 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
- Provide guidance to junior team members on model development and EDA
- Work with Product Manager(s) to intake project/product requirements and translate these to technical tasks within the team’s tooling, technique and procedures
- Continued self-led personal development
- 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
- Knowledge of microservices architecture and continuous delivery concepts in machine learning and related technologies such as helm, Docker and Kubernetes
- Familiarity with Deep Learning techniques for NLP.
- Familiarity with LLMs - using ollama & Langchain
- Excellent verbal and written skills
- Proven collaborator, thriving on teamwork
Preferred qualifications
- Master’s or Doctor of Philosophy degree in Computer Science, Applied Math, Statistics, or a scientific field
- Familiarity with cloud computing platforms (AWS, GCS, Azure)
- Experience with automated supervision/surveillance/compliance tools
About the company
Smarsh hires lifelong learners with a passion for innovating with purpose, humility and humor. Collaboration is at the heart of everything we do. We work closely with the most popular communications platforms and the world’s leading cloud infrastructure platforms. We use the latest in AI/ML technology to help our customers break new ground at scale. We are a global organization that values diversity, and we believe that providing opportunities for everyone to be their authentic self is key to our success. Smarsh leadership, culture, and commitment to developing our people have all garnered Comparably.com Best Places to Work Awards. Come join us and find out what the best work of your career looks like.
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Remote Data Science Nlp Generative Ai Ai