Data Scientist / ML Engineer

Themis•New York, NY

6d•Remote

About The Position

Themis is seeking a Data Scientist / ML Engineer to transform data into actionable intelligence for their governance, risk, and compliance platform. This role involves the entire machine learning lifecycle, from problem definition and data exploration to model deployment and monitoring. The successful candidate will apply machine learning and large language models to compliance workflows, emphasizing accuracy, explainability, and trust in a regulated environment. The ideal candidate is proficient in both analytical notebooks and production code, values rigor and measurable impact, communicates effectively with non-technical stakeholders, and is committed to responsible AI deployment.

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

Nice To Haves

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

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