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Statistics Canada – Canadian Research Data Centre Network (CRDCN) – Data Analytics Internship

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  • Anywhere

Statistics Canada-CRDCN

Are you a graduate student searching for an opportunity to use your data analysis skills?

We are seeking interest among students for a 4-8 month data analysis internship opportunity. The Statistics Canada-CRDCN Data Analytics Internship provides opportunities on analytical projects in program areas across Statistics Canada. This initiative builds analytical capacity for Statistics Canada and offers graduate students opportunities to work on projects that address the information needs of users, promote the use of leading-edge methods and serve Canadians.

Benefits

These internships will give you the opportunity to apply your skills alongside researchers at Statistics Canada on projects of interest to the agency. Benefits of participating in the internship include:

  • gaining work experience while applying and developing your data skills;
  • working with new data sources and contribute to new and innovative methods;
  • working alongside of highly qualified personnel in data analysis;
  • applying knowledge from your areas of study; and
  • being part of a respectful and inclusive work environment.

What types of data analytics opportunities are available?

Opportunities are offered in a variety of program areas such as health, technology and innovation. The project descriptions are listed below (in English only).

If you are interested, please email statcan.asmb-deam.statcan@statcan.gc.ca and include the following information:

  • Name
  • Preferred email address for correspondence
  • Student identification number
  • Current resume/CV
  • Project you are interested in

AItopsies: Investigating Death through Large Language Models

Data source: Canadian Coroner and Medical Examiner Database (CCMED) and/or Canadian Vital Statistics Death Database (CVSD)
Tools that will be used: Large Language Models (LLMs), Retrievers, Natural Language Processing (NLP), R and/or Python

CCMED and the CVSD use data from provincial and territorial partners to produce statistics on mortality and causes of death including opioid deaths. Some of this data is in large unstructured open text fields, which is challenging to code and makes selecting cases for research time consuming. The student will test whether recent advancements in LLMs and related tools (e.g., retrievers) can alleviate these problems. In particular, the student will help develop and test systems for smart search and auto-coding the data.

Measuring inter-generational transfers within the Distributions of Household Economic Accounts

Subject: Tax Filer Simulation and Modelling
Data source: Survey of Financial Security and Various Tax Filings
Tools that will be used: SAS, R, Excel

This project seeks to develop a methodology to link data on household wealth within the Distributions of Household Economic Accounts (DHEA) with indicators on inter-generational transfers available from other Statistics Canada data sources. Users will be better able to identify within the DHEA how access to financial support from a relative may affect households differently in terms of their accumulation of assets and debt obligations. It will also enable greater clarity on the sources of variation in household financial risk and inequality.

The successful applicant will have extensive knowledge of econometrics and experience with applying statistical modelling and analysis techniques (i.e., using software such as SAS, R, Excel, etc.) to manipulate, develop, and analyze information within large micro-data sets (i.e., income tax files, administrative datasets, surveys, etc.).

The Savings Gap: Re-assessing Canadian Household Income and Saving

Subject: Macroeconomic accounts, household savings, financial vulnerability, wealth gap
Data source: SFS, T2 Tax data, T1 tax data, T3 tax data, macro-economic analysis
Tools that will be used: Python, R, Excel, Powerpoint, etc.

Data from the income and expenditure program of the national accounts show that Canadian households are persistent net borrowers, which means they consistently need to borrow funds to match their uses. This net borrowing must equal the net financial investment recorded in the financial flow accounts. When analysing the financial flow accounts and national balance sheet in isolation, Canadian households don’t appear to be net borrowers, certain transactions must be heavily adjusted to preserve the net lending/net financial investment linkage, making them incoherent with the related asset levels.

The primary output of this project will be an analytical and technical report outlining conceptual, methodological and statistical issues (i.e., data gaps) and solutions. This report will be accompanied by documentation, structured data outputs and associated metadata. This underlying information will help justify any revisions to household incomes or support the findings that Canadians are indeed persistent net borrowers.

To apply for this job email your details to statcan.asmb-deam.statcan@statcan.gc.ca