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Scotiabank

Manager, Quantitative Strategies & Analytics

Toronto, ON, M5H 4A9, Canada

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Applications open in a new tab.

Job details

Apply by: Nov 11, 2026
Posting language: English
Posted: Nov 3, 2026

Original job description

Requisition ID: 277365
Employee Referral Program – Potential Reward: $2,000.00

We are committed to investing in our employees and helping you continue your career at Scotiabank.

Purpose

The Manager provides analytical expertise to deliver data-driven insights, modernize and automate processes, and build purpose-built quantitative tools that enhance the investment process and advisor-facing impact. Operating at the intersection of data analytics, finance, and technology, the role requires strong communication skills and the ability to collaborate effectively with partners and senior stakeholders. Success relies on balancing technical depth with business understanding. Reporting to the Senior Manager, this position analyzes data to generate insights, builds analytical tools, and applies quantitative methods to improve investment decision‑making, drive innovation, and support process automation.

Accountabilities

Design, enhance, and maintain models, dashboards, and data‑driven analytical tools, supporting both strategic and day‑to‑day investment needs.
Re-engineer manual workflows into repeatable, automated processes to reduce operational risk, improve controls, and increase scalability.
Integrate AI capabilities into existing workflows to enhance productivity, accelerate decision-making, and modernize legacy processes.
Engage with stakeholders to elicit, clarify, and translate business needs into well-defined analytical, data, or modeling requirements.
Define project scope, objectives, deliverables, timelines, and success criteria for analytical tools, dashboards, and quantitative model initiatives.
Promote and demonstrate alignment with enterprise values, diversity and inclusion principles, and a culture of collaboration.
Ensure alignment with risk management practices, contributing to a risk-aware environment and adherence to policies.
Foster a client‑centric culture, supporting investment teams by delivering timely, data‑driven insights and tools that enhance decision-making.
Uphold Scotiabank’s Values, Code of Conduct, and Global Sales Principles, ensuring effective controls around operational, compliance, conduct, AML/ATF/sanctions, and reputational risk.
Contribute to a high‑performance environment by supporting a people strategy that attracts, retains, and develops talent; fostering an inclusive culture; and demonstrating a coaching-oriented mindset.

Education / Experience

University degree or significant coursework in an analytical field such as Data Science/Engineering, Financial Engineering, Mathematics, Statistics, Computer Science & Engineering, or Business Analytics.
Ability to bridge business and technology teams through requirements gathering, process mapping, solution design, testing, and implementation of operational and data-driven solutions.
Strong programming skills: Python and SQL.
Experience with developing web‑based analytical applications (e.g., using Streamlit, Dash, Flask, FastAPI, Django, or similar frameworks).
Power user of AI tools, with a strong understanding of AI capabilities, limitations, and risks.
Hands-on experience with data visualization and BI tools, such as Power BI, Looker, Streamlit, or similar platforms.
Good understanding of trade lifecycle and reconciliation processes.
Good understanding of investment products, including equities, fixed income, alternatives, and pooled funds.
Excellent problem‑solving abilities, attention to detail, and the ability to manage multiple priorities.

Nice to haves

Experience with relevant investment tools and market data providers (e.g., Bloomberg, FactSet, MorningStar).
Understanding of the Software Development Lifecycle (SDLC), including requirements gathering, solution design, testing, deployment, and post-implementation support.
Experience with GitHub Copilot.

Location(s): Canada : Ontario : Toronto

Scotiabank is a leading bank in the Americas. Guided by our purpose: "for every future", we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.

At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. If you require technical assistance, please click here. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.

Note: All postings in me@Scotiabank will remain live for a minimum of 5 days.

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More about this job

Responsibilities

The manager will design and maintain data-driven analytical tools and models to support investment decision-making. They are also responsible for re-engineering manual workflows into automated processes to improve operational efficiency and scalability.

Requirements

Candidates must have a university degree in an analytical field such as Data Science, Finance, or Computer Science. Strong programming skills in Python and SQL, along with experience in developing analytical applications and BI tools, are required.

Skills

Python
SQL
Data analytics
Financial engineering
Process automation
Data visualization
Business intelligence
Quantitative modeling
Investment analysis
AI tools
Streamlit
Dash
Flask
FastAPI
Power BI
Looker

Benefits

Employee referral program

Visa sponsorship

Not detected in the job text

Keywords

Quantitative strategies
Data science
Financial engineering
Python
SQL
Automation
AI
Power BI
Looker
Streamlit
Dash
Flask
FastAPI
Django
Investment products
Trade lifecycle
Bloomberg
FactSet
MorningStar
SDLC
GitHub Copilot
Data visualization
Business analytics
Risk management
Process mapping
Solution design
Equities
Fixed income
Alternatives
Pooled funds

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