Lead Data Scientist

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Smarsh empowers its customers to manage risk and unleash intelligence in their digital communications. Our growing community of over 6,500 organizations in regulated industries counts on Smarsh every day to help them spot compliance, legal or reputational risks in 80+ communication channels before those risks become regulatory fines or headlines. Relentless innovation has fueled our journey to consistent leadership recognition from analysts like Gartner and Forrester, and our sustained, aggressive growth has landed Smarsh in the annual Inc.

5000 list of fastest‑growing American companies since 2008.

As a Lead Data Scientist (NLP & Financial Compliance) at Smarsh
, you will spearhead the development of state‑of‑the‑art natural language processing (NLP) and large language model (LLM) solutions that power next‑generation compliance and surveillance systems. You’ll work on highly specialized problems at the intersection of NLP, communications intelligence, financial supervision, and regulatory compliance, where unstructured data from emails, chats, voice transcripts, and trade communications hold the keys to uncovering misconduct and risk.

This role will involve working with other Senior Data Scientists and mentoring Associate Data Scientists in analyzing complex data, generating insights, and creating solutions across a variety of tools and platforms. The ideal candidate for this position will possess the ability to perform both independent and team‑based research and generate insights from large data sets with a hands‑on/can‑do attitude of servicing/managing day‑to‑day data requests and analysis.

• 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).
• Develop 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, tensor flow, 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, fast
  
  Text, word2vec) and transformer models (Bert, SBert, Hugging Face, 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.

• Master’s or Doctor of Philosophy degree in Computer Science, Applied Math,…