Senior Data Scientist, Investment Distribution

BMO•Toronto, ON

5d•CA$82,800 - CA$154,800•Onsite

About The Position

A data science specialist position that combines knowledge of financial markets and investment sales with data and technology skills. 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. Develops predictive models and leverages data technology to design solutions that deliver smarter business decisions. Data visualization and dashboard management expected to be part of day-to-day. The ideal candidate will be collaborative and sociable, and will work with data engineers to set up analytics, as well as investment distribution teams to implement analytics solutions. Plays an active role in the futuristic display of data, and advancement of innovative data strategies to understand consumer trends and address business problems. Uses data mining and extracting usable data from valuable data sources to assess feasibility of AI/ML solutions for improved processing and usage of organization data. Conducts large-scale analysis of information to discover patterns and trends by combining different modules and algorithms. Uses analysis to provide recommendations and advice for business leaders to maintain to maintain market competitiveness. Develops prediction systems and machine learning algorithms. Investigates additional technologies and tools for developing innovative data solutions for business stakeholders. Collaborate together with the product team and partners to understand and provide data-driven decision making, business planning and future roadmap. Focus is primarily on business/group within BMO; may have broader, enterprise-wide focus. Provides specialized consulting, analytical and technical support. Exercises judgment to identify, diagnose, and solve problems within given rules. Works independently and regularly handles non-routine situations. Broader work or accountabilities may be assigned as needed.

Requirements

Intermediate level of proficiency: Mathematics, statistics & operations research.
Intermediate level of proficiency: Machine learning.
Intermediate level of proficiency: Trust, bias and ethics.
Intermediate level of proficiency: Creative thinking.
Intermediate level of proficiency: Critical thinking.
Advanced level of proficiency: Big data.
Advanced level of proficiency: Data visualization.
Advanced level of proficiency: Computational thinking and programming.
Advanced level of proficiency: Data wrangling.
Advanced level of proficiency: Data preprocessing.
Advanced level of proficiency: Creative reasoning.
Advanced level of proficiency: Verbal & written communication skills.
Advanced level of proficiency: Collaboration & team skills.
Advanced level of proficiency: Analytical and problem solving skills.
Advanced level of proficiency: Influence skills.
Advanced level of proficiency: Data driven decision making.
Typically between 5 - 7 years of relevant experience and post-secondary degree in related field of study or an equivalent combination of education and experience.
SQL skills are essential.
Python/SAS skills are essential.
Visualization (e.g., PowerBI) skills are essential.

Nice To Haves

Knowledge of asset management, sales, and/or financial institutions an asset.

Responsibilities

Mine and analyze large sets of structured and unstructured data to obtain insights using advanced analytical algorithms and technologies (e.g. machine learning, deep learning, artificial intelligence).
Design and construct new processes for modeling data.
Develop predictive models and leverage data technology to design solutions that deliver smarter business decisions.
Perform data visualization and dashboard management.
Work with data engineers to set up analytics.
Implement analytics solutions with investment distribution teams.
Play an active role in the futuristic display of data and advancement of innovative data strategies to understand consumer trends and address business problems.
Use data mining and extract usable data from valuable data sources to assess feasibility of AI/ML solutions for improved processing and usage of organization data.
Conduct large-scale analysis of information to discover patterns and trends by combining different modules and algorithms.
Provide recommendations and advice for business leaders to maintain market competitiveness.
Develop prediction systems and machine learning algorithms.
Investigate additional technologies and tools for developing innovative data solutions for business stakeholders.
Collaborate with the product team and partners to understand and provide data-driven decision making, business planning and future roadmap.
Provide specialized consulting, analytical and technical support.
Exercise judgment to identify, diagnose, and solve problems within given rules.
Handle non-routine situations independently.