Themis
Data Scientist / ML Engineer
Remote Data Science role with clear candidate location fit.
PostedJul 1, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
USA
Role overview
Data Scientist / ML Engineer
Requirements and responsibilities
Readable role content extracted into sections for faster review.
Modeling & Experimentation
- 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
Production ML & Engineering
- 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
Data & Insight
- 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
Required Qualifications
- 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
Preferred Qualifications
- 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
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