Sr. Data Science Manager - 55520

# Of positions: 2

Business Unit: 4 - FCRM Transformation Delivery and Enablement

Duration: 1 Year

Extension possible: Possibly

Conversion Possible: Possibly

Interview Process: 2 rounds potentially

Work Location: hybrid (wed and any one day)

CANDIDATE PROFILE DETAILS:

Years of experience: 8-10

Reason for request/why opened: additional

% Interaction with Stakeholders: 50%

Team Size: 3+

Project: individuals will focus on model development and data analysis for one of the AML models, customer risk rating and will be working on one project.

Selling Point of the position: supportive and dynamic team, opportunity to be visible, interactions with stakeholders, real hands on exp on developing models for the bank

How will performance be measured: deliverables, SLA, deadlines, quality and feedback

Best vs Average Candidate:

• Strong research, analytical, problem solving, and organizational skills

• Strong communication, writing, and presentation skills,

• Demonstrated time management, project management, and stakeholder management skills,

• Ability to work effectively in a fast-paced environment while demonstrating flexibility to adjust to changing work priorities,

SUMMARY OF DAY-TO-DAY RESPONSIBILITIES:

Department Overview

The Financial Crime Risk Management (FCRM) program is accountable for establishing Anti-Money Laundering and Anti-Terrorist Financing (AML/ATF) programs to ensure compliance with applicable AML/ATF regulatory requirements in jurisdictions where the bank operates.

The Enterprise Model Management (Customer Risk) is one of the core pillars of FCRM program. The mandate of this team focuses on developing, monitoring, enhancing, and managing FCRM's customer risk rating models.

Job Description

We are looking for a Data Science Manager to join Enterprise Model Management (Customer Risk) team and support model development and model management activities. The successful candidate will report to Enterprise Model Management (Customer Risk) Senior Manager. Here is a summary of the responsibilities for this role:

• Develop a deep understanding of business context, objectives, data infrastructure, and help translate business objectives/problems to data-driven action plans and solutions,

• Work closely with Business, FCRM, and Technology stakeholders to understand the rationale and purpose behind each objective, support action plan development, stakeholder alignment, and delivery,

• Use a wide range of programing languages (e.g. Python, SQL, R) and techniques to extract and transform data, apply statistics and analytics, and generate insights,

• Use business intelligence tools such as Tableau or Power BI to visualize data, generate insights, and inform FCRM action plan and decision making,

• Develop predictive models. Assess the performance and effectiveness of different predictive modeling techniques in predicting target variables,

• Document data processing, data analysis, model selection, model performance, impact analysis approach and results,

• Support the Management in presenting model development results to the Executive team, Auditors, Model Validation, and other stakeholders,

• Collaborate with FCRM stakeholders to optimize and enhance customer risk rating models,

• Identify opportunities to streamline and improve any of the existing model development, optimization, and management processes,

• Support relevant activities (documentation, reporting, testing, tuning) to ensure compliance with the bank's Model Risk Management requirements.

MUST HAVE:

• 8+ years of experience in data science, data analytics, and model development,

• Extensive experience in data mining and predictive model development (e.g. Python, R)

• Extensive experience in database querying languages and report development (e.g., SQL),

• Strong storytelling, slide development, and report writing skills

NICE TO HAVE

• Familiarity with AML/ATF regulations is an asset.

• Experience in developing data visualization dashboards (e.g., Tableau, Power BI)

Education - bachelor’s degree in computer science, Statistics, Engineering, or relevant fields