Lead Data Scientist

This is a fully remote position, open to applicants in New York.

📋 Description

• Gather, analyze, and interpret both small and large datasets to reveal valuable insights that aid in the development of statistical methods and machine learning algorithms.

• Oversee the design, training, and implementation of NLP and transformer-based models for applications in financial surveillance and supervision, such as detecting misconduct, market abuse, trade manipulation, and insider communication.

• Create machine learning models and conduct analytics in accordance with established workflows while actively seeking opportunities for optimization and improvement.

• Perform data annotation and conduct quality reviews.

• Engage in exploratory data analysis and analysis of model failure states.

• Contribute to model governance, documentation, and frameworks for explainability that adhere to both internal and regulatory AI standards.

• Provide guidance to clients and prospects during the fine-tuning and development processes of machine learning models and analytics.

• Mentor junior team members in model development and exploratory data analysis.

• Collaborate with Product Manager(s) to gather project and product requirements and translate these into technical tasks using the team’s tools, techniques, and procedures.

• Pursue ongoing self-directed personal development.

⛳️ Requirements

• In-depth knowledge of **financial markets, compliance, surveillance, supervision, or regulatory technology**.

• Experience with one or more data science and machine/deep learning frameworks and tools, such as scikit-learn, H2O, Keras, PyTorch, TensorFlow, pandas, NumPy, caret, and tidyverse.

• Strong grasp of data science and statistical principles (including regression, Bayes, time series, clustering, P/R, AUROC, exploratory data analysis, etc.).

• Comprehensive understanding of key programming concepts (e.g., split-apply-combine, data structures, object-oriented programming).

• Solid foundation in statistics (including hypothesis testing, ANOVA, chi-square tests, etc.).

• Familiarity with NLP transfer learning, including word embedding models (such as GloVe, fastText, word2vec) and transformer models (including BERT, SBERT, HuggingFace, and GPT-x, etc.).

• Experience using natural language processing toolkits such as NLTK, spaCy, and Nvidia NeMo.

• Knowledge of microservices architecture and continuous delivery concepts in machine learning, alongside related technologies like Helm, Docker, and Kubernetes.

• Understanding of Deep Learning techniques for NLP.

• Familiarity with LLMs, including using Ollama & Langchain.

• Excellent verbal and written communication skills.

• Proven ability to collaborate effectively and thrive in a team-oriented environment.

• **Preferred Qualifications**

• Master’s or Doctor of Philosophy degree in Computer Science, Applied Mathematics, Statistics, or a related scientific discipline.

• Familiarity with cloud computing platforms including AWS, GCS, and Azure.

• Experience with automated supervision, surveillance, and compliance tools.

🏝️ Benefits

• Comprehensive health and wellness programs.

• Opportunities for professional development and continuous learning.

• Flexible working arrangements to promote work-life balance.

• Collaborative and inclusive work environment.

• Competitive salary and performance-based incentives.