Data Scientist - Fall Co-op 2024

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Toronto, ON
Hybrid
Internship
Fintech • Machine Learning • Payments • Software • Financial Services
Change everything. Starting with your career.
The Role

161 Bay Street (93021), Canada, Toronto,Toronto, Ontario,
Data Scientist - Fall Co-op 2024
Our Capital One Data Science Team.
Yes, we're a credit card company. But we're more than that too. We're driven by what our customers want, and how to make their lives simpler.
We're always looking for creative ways to offer digital solutions that make sense for our customers. With your help, we'll build the next generation of banking in Canada based on customer-focused values, compelling products and great engineering.
Working with us
How do we do things at Capital One Canada? We listen - to our people, and to our customers. We change with the times and have adopted a flexible hybrid model (remote and/or in-office). A flexible hybrid working model is one that creates the opportunity to match the work that we do to the environment that best supports that work. Associates working in the hybrid model are expected to come into the office 3 days a week across Tuesdays and Thursdays, reserving Mondays and Fridays as company-wide virtual days. As was true before the pandemic, expectations of being in the office will be balanced with personal life flexibility. We recognize that everyone has a unique working pattern so we're open to discussing flexible working arrangements that will best accommodate you.
At Capital One we're committed to diversity, inclusion and belonging . We strive to build a culture where diverse perspectives are valued, innovative ideas are encouraged and inclusive behaviours are embedded in everything we do to positively impact associates. Strive to build a culture where diverse perspectives are valued, innovative ideas are encouraged and to help challenge the status quo and create the best outcomes for everyone.
Capital One Canada is an equal opportunity employer committed to fostering a diverse and inclusive work environment. We consider all qualified applicants and will meet the needs of those requiring reasonable accommodations.
A day in the life of a Capital One Data Science Co-op.
Capital One is more than you think. We're a data-driven tech company that just happens to do credit cards. We focus on customer-first solutions, and we're united with this common goal. We have all the advantages of a startup, with the resources of a big company. Working here means you're curious, innovative spirit will be encouraged as you push boundaries and try new things, while your career is nurtured and supported. Our environment is creative and filled with teams that are passionate about the dynamic work they're doing. We're ahead of other banks in tech and we want to keep it that way.
As a Data Scientist co-op, you will work with cross-functional teams to leverage model solutions to solve complex business problems. As a data savvy individual, you will use your skills to identify the best algorithm or model to solve the problem at hand. You will have autonomy to take ownership and lead an end to end project, from problem identification to solution deployment.
Types of projects the Data Science team is involved in:

  • Using Python and PySpark to analyze millions of transactions to understand and predict fraud patterns
  • Exploring new proprietary data sources and creating predictive models or customer behavior
  • Applying machine learning techniques to build financial risk models
  • Create and test algorithms to optimize search engine marketing
  • Design and develop data pipelines and visualizations to monitor model performance and data quality


Be ready to join a community with some of the most talented people you've ever met, who see the customer first, and want to use their skills to make a difference. And, as a founder-led company, we're inspired to make, break, and do good. So, let's create something great together.
Learn more about our Data Science team here !
Responsibilities:

  • Using Python and PySpark to analyze millions of transactions to understand and predict fraud patterns
  • Exploring new proprietary data sources and creating predictive models or customer behavior
  • Applying machine learning techniques to build financial risk models
  • Create and test algorithms to optimize search engine marketing
  • Design and develop data pipelines and v isualizations to monitor model performance and data quality


Basic Qualifications:

  • Currently enrolled in an undergraduate degree or higher from an accredited university


Preferred Qualifications
(Don't have them all? Don't worry. We'll help you develop the right skills for the job):

  • Pursuing a degree in Mathematics, Statistics, Computer Science, Engineering or another quantitative discipline
  • Experience analyzing and manipulating large data sets using tools such as SQL or Python
  • Experience with statistical analysis and data mining using tools such as Python, R, SAS, Matlab, Stata, or SPSS
  • Experience building and maintaining data pipelines
  • Experience in a Linux/Unix environment, with Git, AWS, or APIs
  • Excellent verbal and written communication skills
  • Previous coop experience


About Capital One Canada
We've been helping millions of Canadians for over 20 years by providing them with access to credit when others wouldn't. We're on a journey to put our customers first, and keep them there, by building modern technology solutions to simplify and humanize the world of credit cards. We have the positive energy of a startup, with the advantages of a big company.
MUST INCLUDE YOUR COVER LETTER, RESUME AND UNOFFICIAL TRANSCRIPT IN ONE PDF DOCUMENT IN THE ATTACHMENT SECTION WHEN YOU CREATE YOUR PROFILE TO APPLY.
We may use your information for automated decision making. We may, for certain purposes, render a decision based exclusively on automated processing of your personal information as a part of the candidate screening process.
Capital One Canada is an equal opportunity employer committed to fostering a diverse and inclusive work environment. We consider all qualified applicants and will meet the needs of those requiring reasonable accommodations.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at [email protected] . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to [email protected]
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

What the Team is Saying

Ryan Page
Kristen Cornelsen
Natalia Bachmann
The Company
HQ: McLean, VA
55,000 Employees
Hybrid Workplace
Year Founded: 1994

What We Do

At Capital One, we think and work like a tech company, using our digital fluency to transform everything about the customer experience. We’re bending data to our will, and turning a stodgy industry on its head. That’s reflected in our ranking as the number one business technology innovator in the U.S. in the 2016 InformationWeek Elite 100.

Why Work With Us

Here’s another question: What are you looking for? A place where curiosity is the starting point? Where data leads to human insights? Where humanity drives product development? We’re bringing breakthrough products and services to consumers, small businesses, and commercial clients. And each new idea makes life better for millions of people.

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Capital One Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

Typical time on-site: Not Specified
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