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Data Science in the Government of Canada

What is Data Science?

Data Science is an interdisciplinary field that uses scientific methods and algorithms to extract information and insights from diverse data types. It combines domain expertise, programming skills and knowledge of mathematics and statistics to solve analytically complex problems.

You can get a short overview of the daily work of a data scientist in the Government of Canada by watching this video.

The COVID-19 Pandemic: A Stark Reminder of the Crucial Role of Data Scientists

Data science allows government agencies and department to respond quickly to changing economic and social situations. For example, Statistics Canada is using the power of data science to support the COVID-19 response in Canada.

The agency collaborated with Health Canada to visualize the supply and demand information for Personal Protective Equipment (PPE). Before the data visualization could begin, the data needed to be extracted and ingested. The data were coming daily from many different sources (different provincial/territorial governments, other federal departments and private sector companies that had been hired to help source the PPE) and in many different formats (e.g. Word documents, Excel files, PDFs) and required a significant amount of manual work to create standardized reports.

To improve this process, data scientists at Statistics Canada created an algorithm that parses the data into different data entries. Machine learning was used to identify numbers and dates within the text. The structured data were then presented in a PowerBI dashboard that was shared with other government departments to meet their information needs and better understand the supply and demand for PPE in Canada.

Source: Data Science Centre, Statistics Canada, 2022-09-29

Data Scientist Job Profiles in the Government of Canada

Data Science Analyst

Data scientists use data to identify and solve complex business problems. They have an interdisciplinary focus, using techniques and knowledge from a range of scientific and computer science disciplines (for example, economics, statistics, mathematics, predictive analytics, and machine learning) and are generally part of multidisciplinary project teams involving data science engineers, business owners, social scientists, business analysts, project managers, software engineers/designers, and others. The roles and responsibilities of a data science analyst may include:

● eliciting problems from business owners, understanding where data science can add value in supporting strategic and operational decision making, and designing data science solutions and metrics to these problems;

● Clean, process, and explore structured and unstructured data to extract actionable insights for making business decisions;

● building and validating statistical models from data, often using advanced statistical techniques such as econometrics, machine learning, predictive analytics, regression, segmentation, and other relevant techniques;

● supporting computer scientists and data engineers conducting the deployment and maintenance of the models;

● using best coding practices to generate reproducible, verifiable work;

● exploring and visualising data to present the ‘story’ of data, based on a thorough understanding of business processes and incentivized behaviour, in a meaningful way to a range of technical and non-technical audiences;

● using an evolving range of data analysis tools and techniques, including open source, some of which must be learnt quickly, as and when required;

● adhering to standards, guidelines and norms around digital solutions and responsible development and implementation of artificial intelligence and machine learning.

Data Science Engineer

Data science engineers work alongside data scientists to feed, deploy, monitor and maintain models and other data products. They have understanding in data science as well as computer science expertise pertaining to production systems operations (DataOps/MLOps). The roles and responsibilities of a data science engineer may include:

● prototyping and demonstrating solutions for clients in customer environments to enable further development;

● developing end-to-end (Data/MLOps) pipelines based on an in-depth understanding of cloud platforms, AI lifecycle, and business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably;

● building automation software to operate systems needed for data storage, data management, data science notebooks, distributed training, model repository, feature repository, continuous delivery, model serving, and monitoring;

● operating production AI systems and making sure they are available, scalable, and performant;

● building and sharing the technical expertise necessary to analyze and recommend enterprise-grade solutions for operationalizing AI or advanced analytical models;

● communicating best practices and tools among data science teams in order to improve productivity and avoid common mistakes;

● assembling different pieces in order to build an end-to-end, reliable, enterprise-grade production system;

● setting the required architecture and deployment processes for AI, from data ingestion to production and maintenance;

● providing technical advice to management and other scientists as it pertains to the operationalization of models.

Data Visualization Specialist

Data visualization specialists make large and/or complex data more accessible, understandable and usable. They deliver data in a useful and appealing way to end users. This requires expertise at translating data and statistical outputs in ways that are useful for both subject matter experts and business users. The roles and responsibilities of a data visualization specialist may include:

● eliciting the needs from end users in terms of data visualizations and related features (e.g., interactivity);

● accessing and manipulating data from different sources, for example using flat files or Structured Query Language (SQL) queries;

● developing dashboards, infographics, and interactive visualisations using different software, including common business intelligence tools (e.g., PowerBI, Tableau, PowerPoint) and/or specialized libraries (e.g., D3.js, seaborn, plotly);

● understanding and applying best practices for design of data visualizations; and

● communicating best data visualization practices and tools among data science teams, to avoid common mistakes and make data visualizations more effective;

● manage, clean, abstract, and aggregate data alongside a range of analytical studies on that data;

● manipulate and link different data sets;

● utilize storytelling techniques to communicate analysis results and impact.