Data Scientist

As a Data Scientist II, you will serve in a high‑impact "bridge" role between core model research teams and customer‑facing data science operations. Your primary objective is to accelerate the transition from model development to real‑world deployment by building automated analysis frameworks and monitoring systems that directly support customer success. Beyond model development, you will create the technical connective tissue-high‑performance pipelines, analytical tooling, and custom dashboards-that enables faster client onboarding, effective issue resolution, and continuous model improvement through closed‑loop feedback.

• Develop Analysis Frameworks: Design and maintain scalable analytical frameworks that enable rapid, effective troubleshooting during proofs of concept, onboarding phases, and ongoing production support.
• Bridge Research and Operations: Act as the primary technical liaison between core modeling teams and customer application teams, translating complex model behavior into actionable insights for operational use.
• Build Monitoring & Alerting Systems: Create and manage dashboards and real‑time alerting systems to track model performance, detect drift, and surface false positives/negatives at granular segment levels.
• Drive Closed‑Loop Model Improvement: Monitor production data and systematically feed real‑world outcomes back into the model development lifecycle.
• Enhance Internal Data Pipelines: Build and optimize end‑to‑end data pipelines using distributed processing and orchestration tools to support automated reporting and performance evaluation.
• Collaborate Cross‑Functionally: Work closely with Product, Engineering, and Compliance partners to translate user needs and regulatory requirements into measurable machine‑learning objectives.
  
  

• Education & Experience: Bachelor's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics) with 3-5 years of relevant experience, or a Master's/PhD with 1-3 years of experience.
• Technical Skills: Strong proficiency in Python and SQL, with hands‑on experience using distributed data processing platforms.
• Pipelines & Infrastructure: Experience building orchestrated workflows and familiarity with cloud environments and infrastructure‑as‑code concepts.
• Analytical Rigor: Strong foundation in statistics, hypothesis testing, and model evaluation metrics such as precision, recall, F‑beta, and ROC‑AUC.
• ML & Data Engineering Experience: Practical experience with data curation, labeling strategies, and implementing automated data quality checks.
• Communication: Ability to clearly convey complex technical concepts to both technical peers and non‑technical stakeholders.
• Domain Interest: Strong interest in NLP, large language models, and applied machine learning in regulated or compliance‑focused environments.
  
  

• Experience with real‑time data indexing and search technologies.
• Background in fraud detection, identity verification, sanctions screening, or other risk‑focused domains.
• Prior experience in customer‑facing data science, solutions engineering, or internal tooling roles supporting external stakeholders.