Key skills
PythonSQLAIMachine LearningMLLLMLarge Language ModelsPyTorchTensorFlowscikit-learnPandasMLOpsAWSAzureGCPLeadershipCommunication
About this role
Themis is a collaborative governance, risk, and compliance platform helping banks, credit unions, and fintechs streamline oversight. They are seeking a Data Scientist / ML Engineer to turn data into intelligence for governance, risk, and compliance, working across the full lifecycle of problem framing, data exploration, and model deployment.
Responsibilities:
- Frame ambiguous compliance and risk problems as well-defined data and modeling tasks
- Build, evaluate, and iterate on machine learning models and LLM-powered features
- Design experiments and define metrics that measure real impact on customer workflows
- Apply rigorous evaluation, including accuracy, explainability, and bias considerations appropriate to a regulated domain
- Build and maintain data and ML pipelines for training, inference, and monitoring
- Deploy models and AI features into production and monitor their performance over time
- Collaborate with engineering to integrate models into the Themis platform reliably and at scale
- Implement guardrails, evaluation harnesses, and monitoring for AI-powered features
- Explore and prepare data, build features, and ensure data quality and integrity
- Translate data and model findings into clear recommendations for product and leadership
- Partner with Product to identify high-value opportunities for ML and AI
Requirements:
- Strong foundation in machine learning, statistics, and data science fundamentals
- Proficiency in Python and common data and ML libraries (e.g., pandas, scikit-learn, PyTorch, or TensorFlow)
- Experience taking models or data products from prototype to production
- Experience with SQL and working with real-world, messy data
- Ability to design experiments, define metrics, and evaluate models rigorously
- Strong communication skills and the ability to explain technical work to non-technical stakeholders
- Ability to manage ambiguity and own problems end to end
- Experience building with large language models, retrieval-augmented generation, or modern AI tooling
- Experience deploying and monitoring ML in production (MLOps)
- Experience in financial services, compliance, risk, fraud, or other regulated or high-stakes domains
- Familiarity with cloud data and ML platforms (e.g., AWS, GCP, or Azure)
- Experience working in a startup or high-growth company