Job Description

Requisition ID: 163034

Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.

Scotiabank's Product Economics mandate is to leverage advanced analytics to improve Customer profitability through optimized and forward looking pricing decisions that consider the value to the Bank as well as perceived value to our Customers. The team sits within Scotiabank's One Retail Bank organization.

About the Manager, Financial Pricing Analytics (Data Scientist)

The Product Economics team is looking for a data scientist that will support the revenue and volume growth for each of the products (including but not limited to, lending products such as Mortgages, Lines of Credit, Credit Cards, Loans and deposits such as Savings Accounts and GICs) through providing advanced analytical pricing solutions, and influences Scotia's strategic decisions on consumer pricing (rates / fees) and other consumer behaviour initiatives.

We will provide you the opportunities to gain exposure to different businesses across the organization, a wide variety of analytical tools, an innovative work environment and expertise guidance to achieve both business and personal goals. You will be building predictive models using Python, R, PySpark and SAS and working with big data stored in Hadoop and relational databases. You will also have exposure to Cloud computing (Microsoft Azure or Google cloud). In addition, you will assist in designing optimization engines to recommend optimal pricing for a suite of financial products (including deposits and lending).

Job Responsibilities

• You will extract and cleanse large datasets:

• Integrate data across a variety of data stores / platforms (eg. DB2, SQL server, SAS in Unix and Hive in Hadoop) in a way that helps building advanced analytical models
• Leverage distributed computing tools (e.g. Spark, Hadoop) for analysis, data mining and modeling
• Explore data sourced from other environments including (but not limited to) the data lake; apply newly available data to pricing problems (ie. flow of funds, transcribed calls, network analytics data etc.)
• Internal and external data source evaluation
• You will design and build predictive models that explain the customer behavior over the product life cycle:

• Origination models such as response, utilization and attrition modeling
• Portfolio management models such as renewal models, re-pricing models, credit limit optimization, balance transfer and campaign acquisition models
• Portfolio segmentation/customer sensitivity modeling
• Performing revenue optimization for a chosen portfolio. You need to understand business objectives, translate them into mathematical optimization problems, create profit function and recommend optimal pricing for each product
• Create and apply model and algorithm testing strategies to conduct multi-variate testing and A/B testing to measure effectiveness of models and make ongoing changes
• Model validation
• You will advance the Produce Economics competency:

• Collaborate with business lines and other stakeholders and identify opportunities to drive business value and influence future pricing strategy by leveraging Data Science
• Provide subject matter expertise on predictive modelling, data mining, statistical analysis and machine learning to Product Economics internal customers
• Effectively communicate results of highly technical projects to business audiences

Job Requirements:

• You have excellent problem solving and analytical skills (previous experience in an analyst function is required)
• You have good communication skills, and you can translate complex technical information to a non-technical audience
• You have good time management skills and are able to meet timelines
• You have an analytical background (Applied Math, Statistics, Physics, Engineering, Computer Science)
• It would be great if you also held a Masters or PHD in mathematics, statistics or a related discipline
• You have strong programming skills, ideally in Python or R (C++, Java or other programming languages would also be great)
• You have solid SQL skills for querying relational databases (SAS, SQL Server, DB2, MySQL)
• You have strong theoretical knowledge and practical understanding of statistical analysis and predictive modeling

It would be great if you also:

• Have experience with common statistical and machine learning libraries in Python, R, Spark (Keras/Tensorflow, Sklean)
• Are familiar with Hadoop Big Data ecosystem (Hive, Spark, Pig, Sqoop)
• Are familiar with Cloud computing (Microsoft Azure or Google cloud)

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 . 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.