Resumen del rol

Data Scientist / ML Engineer

Requisitos y responsabilidades

Contenido del rol extraído en secciones para revisar más rápido.

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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FocoData ScienceÁrea del rol
Señal de senioritySeniorNivel del candidato
StackAWS, Azure, GCPSkills principales
Ubicación1 país aceptadoElegibilidad

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