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Campagne de recrutement de scientifiques des données - Page d'information pour les candidats/Data Science GC (view source)
Revision as of 09:52, 26 October 2022
, 09:52, 26 October 2022no edit summary
=== Data Science Analyst ===
=== 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.
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.
