Senior Data Scientist

QIC digital hub

Research and Model Development

• Analyze business tasks and formulate ML problem statements together with product teams
• Exploratory Data Analysis: exploring insurance data, identifying patterns and insights
• Feature Engineering: creating features from raw data (transactions, policies, claims, behavioral data)
• Model Prototyping: rapid validation of hypotheses and MVP solutions
• A/B Testing: designing experiments to validate ML solutions

Production Development

• Model deployment to production: from Jupyter notebook to production API
• ML pipeline setup: automation of training, validation, and deployment
• Real-time inference: integrating models into business processes (underwriting, claims processing)
• Model monitoring: tracking performance, drift detection, alert systems
• Performance optimization: accelerating inference, scaling workloads

Technical Skills

• Background with financial/insurance data is an advantage
• 5+ years of experience in ML/DS with a focus on product development
• Programming languages: Python (advanced level), SQL
• ML stack: scikit-learn, XGBoost, LightGBM, CatBoost, pandas, numpy
• Deep Learning: TensorFlow or PyTorch for NLP and recommendation systems
• MLOps: experience deploying models into production (Docker, CI/CD, monitoring)
• Big Data: PySpark or similar tools for large-scale datasets
• Git and collaborative development

Professional Competencies

• Feature Engineering: creating and selecting features for various types of data
• Model Validation: cross-validation, A/B testing, quality metrics
• Statistics: probability theory, hypothesis testing, confidence intervals
• Working with imbalanced data (critical for anti-fraud tasks)
• Time Series Analysis: forecasting and trend analysis
• Model interpretability: SHAP, LIME to explain results to business stakeholders