Smarsh Sites

Smarsh Sites

Lead Data Scientist

Atlanta · Senior

Sponsorship not specified$166k-$214kDetected 34 days ago
Object-Oriented ProgrammingData StructuresAWSAzureCloud PlatformsDockerKubernetesHelmCI/CDMachine LearningDeep LearningTensorFlowPyTorchscikit-learnPandasNumPyData AnalysisData ScienceNLPLLMsStatisticsA/B TestingComplianceProduct Management

About the role

  • 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.
  • 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.

Requirements

  • Strong understanding of financial markets, compliance, surveillance, supervision, or regulatory technology
  • Strong knowledge of key programming concepts (e.g. split-apply-combine, data structures, object-oriented programming)
  • Experience with natural language processing toolkits like NLTK, spaCy, Nvidia NeMo
  • Familiarity with LLMs - using ollama & Langchain
  • Proven collaborator, thriving on teamwork

Nice to have

  • 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
  • We work closely with the most popular communications platforms and the world's leading cloud infrastructure platforms.
  • Come join us and find out what the best work of your career looks like.

Compensation

  • 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.

This listing is sourced directly from Smarsh Sites's careers page and normalized into a canonical job model.