Lead Data Scientist

Role overview

• check_circle Collect, analyze, and interpret small/large datasets to uncover meaningful insights to support the development of statistical methods / machine learning algorithms.
• check_circle 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).
• check_circle Development of machine learning models and other analytics following established workflows, while also looking for optimization and improvement opportunities
• check_circle Data annotation and quality review
• check_circle Exploratory data analysis and model fail state analysis
• check_circle Contribute to model governance, documentation, and explainability frameworks aligned with internal and regulatory AI standards.
• check_circle Client/prospect guidance in machine learning model and analytic fine-tuning/development processes
• check_circle Provide guidance to junior team members on model development and EDA
• check_circle Work with Product Manager(s) to intake project/product requirements and translate these to technical tasks within the team’s tooling, technique and procedures
• check_circle Continued self-led personal development
• check_circle Strong understanding of financial markets, compliance, surveillance, supervision, or regulatory technology
• check_circle 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
• check_circle Command of data science and statistics principles (regression, Bayes, time series, clustering, P/R, AUROC, exploratory data analysis etc…)
• check_circle Strong knowledge of key programming concepts (e.g. split-apply-combine, data structures, object-oriented programming)
• check_circle Solid statistics knowledge (hypothesis testing, ANOVA, chi-square tests, etc…)
• check_circle Knowledge of NLP transfer learning, including word embedding models (gloVe, fastText, word2vec) and transformer models (Bert, SBert, HuggingFace, and GPT-x etc.)
• check_circle Experience with natural language processing toolkits like NLTK, spaCy, Nvidia NeMo
• check_circle Knowledge of microservices architecture and continuous delivery concepts in machine learning and related technologies such as helm, Docker and Kubernetes
• check_circle Familiarity with Deep Learning techniques for NLP.
• check_circle Familiarity with LLMs - using ollama & Langchain
• check_circle Excellent verbal and written skills
• check_circle 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