Quantitative Analytics Specialist

Job description

**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.