Lead Data Scientist job opportunity at Smarsh.

Smarsh Lead Data Scientist

Experience: 2-years

Pattern: remote

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Data science

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PhD

Atlanta / New ..........United States Of America

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. __ How will you contribute? 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 __ What will you bring? 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

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Lead Data Scientist job opportunity at Smarsh.

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