Manager, Liquidity Research and Analytics

at  BMO Financial Group

100 King Street West Toronto Ontario,M5X 1A1
The Manager, Liquidity Research & Analytics reports to the Director, Liquidity Research & Analytics, and plays a key role in the development of quantitative methodologies to inform behavioral assumptions for use in Internal Liquidity Stress Test which is a critical tool for senior leaders to manage the bank’s liquidity risk. Successful candidates will be proficient in handling large volumes of data using a programming language such as SQL, SAS, R, Python and using critical thinking and problem-solving skills to develop an in-depth understanding of BMO’s products and customers to inform liquidity risk management. The role will also be responsible for creating visualizations for dissemination of their analyses using tools such as Power-BI.

Applies knowledge of advanced analytic algorithms and technologies (e.g. machine learning, deep learning, artificial intelligence) to deliver better predictions and/or intelligent automation that enables smarter business decisions, improved customer experience, and drives productivity. Applies strong communication and story-telling skills to summarize statistical/algorithmic findings, draw business conclusions, and present actionable insight in a way that resonates with business/groups. Drives 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.

• Develops analytical solutions and makes recommendations based on an understanding of the business strategy and stakeholder needs.
• Provides advice and guidance to assigned business/group on implementation of analytical solutions.
• Works with stakeholders to identify the business requirements, understand distinct problems and expected outcomes, and models and frames business scenarios which impact critical business processes and/or decisions.
• 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.
• 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.
• Documents data flow, systems and processes in data collection to improve efficiency and apply use cases.
• Performs experimental design approaches to validate finding or test hypotheses.
• Uses the appropriate algorithms to discover patterns.
• Builds effective relationships with internal/external stakeholders and ensures alignment.
• Supports development of tools and delivers training for data analytics and AI.
• Supports development and execution of strategic initiatives in collaboration with internal and external stakeholders.
• Leads/participates in the design, implementation and management of core business/group processes.
• Focus is primarily on business/group within BMO; may have broader, enterprise-wide focus.
• Exercises judgment to identify, diagnose, and solve problems within given rules.
• Works independently on a range of complex tasks, which may include unique situations.
• Broader work or accountabilities may be assigned as needed.

QUALIFICATIONS:

• Typically between 5 - 7 years of relevant experience and post-secondary degree in a quantitative discipline such as Financial Engineering, Data Science, Statistics, Economics, Natural Sciences or other related field of study or an equivalent combination of education and experience.
• Knowledge of visualization techniques and concepts.
• 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).
• Experience with programming languages (e.g. SQL, Python, R, SAS, SPSS, , Perl) and machine learning /deep learning algorithms/packages (e.g. XGBoost, H2O, SparkML).
• Experience in statistical analysis, data mining, and data cleansing / transformation.
• Technical proficiency gained through education and/or business experience.
• Verbal & written communication skills - In-depth.
• Collaboration & team skills - In-depth.
• Analytical and problem solving skills - In-depth.
• Influence skills - In-depth.
• Data driven decision making - In-depth.

Please refer the Job description for details