BMO Financial Group is looking to solve problems related to asset liability, liquidity, and interest rate risk management, customer analytics, profitability, and stress testing under various macro-economic scenarios by developing, enhancing, implementing, and maintaining quantitative models and analytics suites.
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).
- Effectively apply knowledge of advanced analytic algorithms and modeling techniques (e.g. large data processing, statistical modeling, machine learning) to deliver better predictions and/or intelligent automation that enables smarter business decisions, improved customer experience, and drives productivity.
- Drive innovation through the development of Data & AI products that can be leveraged across the organization and establishes best practices in in alignment with Data & AI governance frameworks of BMO.
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.
- Works with stakeholders to identify the business requirements, understand distinct problems and expected outcomes. Develops analytical solutions and makes recommendations based on an understanding of the business strategy and stakeholder needs.
Other
- Confidently and clearly communicate and summarize statistical/algorithmic findings. Draw business conclusions and present actionable insight in a way that resonates with business/groups (i.e., story-telling skills)
- Provides timely ad-hoc analytical support to business and other key stakeholders
- Documents data flow, systems and processes in data collection to improve efficiency and apply use cases.
- Builds effective relationships with internal/external stakeholders and ensures alignment. Provides advice and guidance to assigned business/group on implementation of analytical solutions.
- Supports development and execution of strategic initiatives in collaboration with internal and external stakeholders.
