Senior Analyst, Data Sciences

Location: Downtown Toronto (Central Bay Street)

Final date to receive applications:

Address:

100 King Street West

Job Family Group:

Data Analytics & Reporting

The Senior Analyst, Quantitative Analysis, Strategy and Insights in Corporate Treasury is ideal for candidate who wants to work on developing, enhancing, implementing and maintaining quantitative models and analytics suites. They would do so by using conventional econometric and machine learning techniques, for the purposes of asset liability, liquidity, and interest rate risk management, customer analytics, profitability, and stress testing under various macro-economic scenarios.

This involves analyzing large account-level and transaction-level data, articulating the problem statement, and specifying the most appropriate quantitative solution.

The successful candidate is someone who can:  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.
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)
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.
Documents data flow, systems and processes in data collection to improve efficiency and apply use cases.
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.
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.
Leads/participates in the design, implementation and management of core business/group processes.
Broader work or accountabilities may be assigned as needed.

Qualifications:

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.
Experience in statistical analysis, data mining, and data cleansing / transformation.
Knowledge of visualization techniques and concepts (e.g, Power BI, Spot Fire).

Experience with programming languages (e.g. SQL, Python, R, SAS, SPSS, Matlab) and machine learning /deep learning algorithms/packages (e.g. XGBoost, H2O, Spark

ML).
Knowledge of distributed computing and/or distributed databases.

Experience with distributed computing language (e.g. Hive / Hadoop/ Spark) & cloud technologies (e.g. AWS Sagemaker, Azure

ML).
Exercises judgment to identify, diagnose, and solve problems within given rules.
Works independently on a range of complex tasks, which may include unique situations.
Data driven decision making - In-depth.
Verbal & written communication skills - In-depth.
Collaboration & team skills - In-depth.
Analytical and problem solving skills - In-depth.
Influence skills - In-depth.
Technical proficiency gained through education and/or business experience.

Uses advanced analytical algorithms and technologies (e.g. machine learning, deep learning, artificial intelligence) to mine and analyze large sets of structured and unstructured data to obtain insights. Designs and constructs new processes for modeling data.…