Job Purpose

GRM Retail Credit Risk aims to be an industry leader in developing innovative solutions for Retail and Small Business Banking. This is accomplished through the use of advanced analytics, agile principles and disciplined risk governance. To align with its departmental mission to modernize Risk Management, the Analytics COE acts as an idea incubator and analytical use cases accelerator, leveraging non-traditional data, advanced tools and enterprise systems. The team relies on Data Science expertise and key business partners to provide consulting services within GRM and help build the next generation of credit solutions at Scotiabank.

The Data Scientist will support key projects aimed at accelerating benefits for shareholders, leveraging enterprise-level data engineering and machine learning tools. She/he will work closely with Global Risk teams, business lines, Digital Banking and IT to explore ideas and apply advanced analytics techniques to enhance retail lending portfolios within risk appetite thresholds. The candidate will help identify and recommend opportunities to deliver innovative credit offers based on risk adjusted return (RAR) and customer lifetime value (CLV) frameworks.

The Data Scientist role requires rigorous logical thinking, curiosity, flexibility and great teamwork abilities. The candidate will be at the intersection of math/stats, computer science/machine learning, communication, and domain knowledge in Risk Management, Digital Banking or Data Product Management. She/he will take a supporting role in agile delivery of POCs to drive innovation and digital transformation throughout the global footprint of Bank and Global Risk Management.
What’s in it for you?
Opportunity to make an impact in the digital transformation of Scotiabank
Exposure to different business lines where analytics techniques are being applied
Hands-on practical projects which provide an opportunity to gain new knowledge and develop skills
A compensation program with competitive salary, opportunities for annual performance incentives based on performance thresholds, a competitive benefits program and continuing education programs

Key Accountabilities
Work in an Agile environment to deploy new credit solutions within 90-120 days
Collaborate with GRM Analytics key stakeholders and partners to define machine learning and Artificial Intelligence (ML/AI) best-practices for GRM use cases
Lead Research & Development work focused on scalable solutions based on RAR and CLV
Support ML/AI blueprint design and strategy optimization frameworks for end-to-end pre-approvals, global limits and risk-based pricing through full credit lifecycle within Risk Appetite thresholds
Support GRM use-case agile delivery, ensure proper controls and ethics in AI systems, as well as customer/financial benefits tracking and communication
Support rapid labs with strong business acumen in supporting the business lines with risk proposals to implement new origination, account management, collections strategies and/or other credit solutions




Functional Competencies
Ability to ingest and work with large volumes of structured and unstructured non-traditional data
Knowledge of strategy optimization leveraging operations research principles would be an asset
Working knowledge of visualization tools such as Tableau and Power BI would be an asset
Strong collaboration skills with ability to translate technical knowledge into business value
Effective communication skills with ability to prepare project documentation and presentations

Education/Work Experience
University degree in relevant STEM discipline (Science, Technology, Engineering and Mathematics)
Working experience with big data tools such as SQL, Hive, Spark
Working experience with open-source programming languages such as Python, R, Scala
Working experience with ML/AI techniques for strategy design (supervised, unsupervised, NLP, reinforcement learning, recommendation engines, APIs)
Working experience with cloud computing platforms such as MS Azure and Google Cloud
Working experience with DevOps principles and/or software engineering best practices would be an asset

Educational level:


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