Quantitative Analytics Specialist

Key Responsibilities

• Model Development & Implementation: Design, develop, and calibrate complex analytical models using Python to address business initiatives and risk management needs.

• Production Monitoring: Establish and execute automated model monitoring programs to track data health, performance, stability, and risk metrics.

• Performance Optimization: Use Python libraries (NumPy, pandas, SciPy) to tune algorithms for low-latency execution and high-performance computing.

• Workflow Automation: Build end-to-end data pipelines and automated reporting workflows to eliminate manual effort in analytics.

• Anomaly Detection: Proactively identify data gaps, model drift, and anomalies, performing root-cause analysis to remediate production issues.

• Stakeholder Collaboration: Partner with data scientists, risk managers, and business leads to translate research models into production-ready software.

Technical Skills Required

• Programming Mastery: Expert-level Python proficiency, including multi-process architecture and class-based design.

• Quantitative Analytics: Strong foundation in mathematics and statistics (calculus, linear algebra, probability theory) and time-series analysis.

• Model Lifecycle Management: Experience with model risk management (MRM) standards and model monitoring frameworks.

• Data Engineering: Proficiency in SQL for complex querying and experience with big data tools like Spark, Kafka, or Hadoop.

• ML & AI Frameworks: Hands-on experience with PyTorch, TensorFlow, or scikit-learn for building and evaluating models.

• DevOps & Deployment: Familiarity with CI/CD pipelines, Git-based version control, and containerization tools like Docker or Kubernetes.

Preferred Qualifications

• Education: Bachelor’s or Master’s degree in a quantitative field such as Computer Science, Mathematics, Finance, or Engineering.

• Domain Expertise: Knowledge of financial products, market risk, or regulatory standards (e.g., Basel, MRM policy).

• Analytical Tools: Proficiency in visualization platforms like Power BI, Tableau, or Grafana for performance tracking.