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Smarsh empowers its customers to manage risk and unleash intelligence in their digital communications. Our growing community of over 6500 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 natural language processing, 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.

The role will involve working with other Senior Data Scientists and mentoring Associate Data Scientists in analyzing complex data, generating insights, and creating solutions as needed across a variety of tools and platforms. This role demands both technical excellence in NLP modeling and a deep understanding of financial domain behavior—including insider trading, market manipulation, off‑channel communications, MNPI, bribery, and other supervisory risk areas. 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.

This role also offers a unique opportunity to get exposure to many problems and solutions associated with taking machine learning and analytics research to production. On any given day, you will have the opportunity to interface with business leaders, machine learning researchers, data engineers, platform engineers, data scientists and many more, enabling you to level up in true end‑to‑end data science proficiency.

• 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.

• 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.

$166,000 - $214,000 a year

The above salary range represents Smarsh's good faith and reasonable estimate of the range of possible base compensation at the time of posting. Any applicable bonus programs will be discussed during the recruiting process. The salary for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, location, specialty and training. Local cost of living assessments are done for each new hire at the time of offer.

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.