The business problem that BMO Financial Group is trying to solve is to develop, enhance, implement and maintain quantitative models and analytics suites for asset liability, liquidity, and interest rate risk management, customer analytics, profitability, and stress testing under various macro-economic scenarios.
Requirements
- Experience in statistical analysis, data mining, and data cleansing / transformation.
- Knowledge of visualization techniques and concepts (e.g, Power BI, SpotFire).
- Experience with programming languages (e.g. SQL, Python, R, SAS, SPSS, Matlab) and machine learning /deep learning algorithms/packages (e.g. XGBoost, H2O, SparkML).
- Knowledge of distributed computing and/or distributed databases. Experience with distributed computing language (e.g. Hive / Hadoop/ Spark) & cloud technologies (e.g. AWS Sagemaker, AzureML).
- Deep learning.
- Machine learning.
- Trust, bias and ethics.
- Mathematics, statistics & operations research.
- Big data.
Responsibilities
- Applies scripting / programming skills to assemble various types of source data (unstructured, semi-structured, and structured) into well-prepared datasets with multiple levels of granularities (e.g., demographics, customers, products, transactions).
- Develops agreed analytical solution by applying suitable statistical & machine learning techniques (e.g., A/B testing, prototype solutions, mathematical models, algorithms, machine learning, deep learning, artificial intelligence) to test, verify, refine hypotheses.
- Summarizes statistical findings and draws conclusions, presents actionable business recommendations. Presents findings & recommendations in a simple, clear way to drive action.
- Uses the appropriate algorithms to discover patterns. Performs experimental design approaches to validate finding or test hypotheses
- Automates and enhances processes to generate scheduled reports — detailing accurate balance sheet position and actionable analytical insights to stakeholders in an efficient and timely manner
- Works with various data owners to discover and select available data from internal sources and external vendors (e.g. lending system, payment system, external credit rating system, and alternative data) to fulfill analytical needs.
- Documents data flow, systems and processes in data collection to improve efficiency and apply use cases.
Other
- Typically between 1-2 years of relevant experience and graduate-level degree in related field of study or an equivalent combination of education and experience.
- Data driven decision making - In-depth.
- Verbal & written communication skills - In-depth.
- Collaboration & team skills - In-depth.
- Analytical and problem solving skills - In-depth.
