{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Kurtis Pykes",
  "title_listed": "Defining a Taxonomy of Roles in The Data Discipline",
  "keywords": "数据岗位,职责",
  "domain": "机器学习",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/defining-a-taxonomy-of-roles-in-the-data-discipline-5c801c5d9d7/",
  "publish_date": "2022-05",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "f45758976f66",
  "collected_at": "2026-10-01T23:51:50",
  "title": "Defining a Taxonomy of Roles in The Data Discipline | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# Defining a Taxonomy of Roles in The Data Discipline\nWhat They Do & Their Key Responsibilities\n[Kurtis Pykes](https://towardsdatascience.com/author/kurtispykes/)\nMay 11, 20226 min read\n![Photo by Brooke Cagle on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/05/0sPWbm-0krExcNgLj-scaled.jpg)\nPhoto by [Brooke Cagle](https://unsplash.com/@brookecagle?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nThe exponential growth in the amount of data we generate has opened the door to several opportunities. \"Data is the new oil,\" you'd often hear at modern business events, and the businesses have been acting on it. Several job roles have since risen to push the data revolution in various industries.\nTo many, the titles in the data discipline aren't as significant as you might expect. The day-to-day workflow of people with the same title but at different companies can vary quite a bit - I wouldn't go as far as completely ignoring them though. Slowly but surely we are coming to terms that unicorns don't exist in the data field and no single person can do all it takes to effectively use data in an organization.\nConsequently, companies have broken up responsibilities into more specialized roles. Understanding the general responsibilities of each role is extremely important at the beginning of your career as it will give you an insight into the necessary tools and skills you must acquire to be suitable for a role.\nWith that said, let's take a look at some of the main roles involved:\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Computer Science](https://roadmap.sh/computer-science) roadmaps.\n## Data Analyst\nThe end goal of data analysis is to come up with **a solution to a business problem** : they seek to increase the efficiency and performance of an organization by discovering patterns in data that can be used to make strategic decisions. Thus, the data analyst uses data to tell narratives that can help businesses make more informed decisions based on data.\nData analysts are also expected to have exceptional communication skills across a variety of mediums including visual, written, and verbal as it's necessary to report their conclusions.\n### **Key responsibilities**\n  * Collaborating with other team members to improve the data collection process and quality.\n  * Creating dashboards and reports.\n  * Performing data analysis and reporting conclusions on areas that can be improved to increase efficiency in an organization or project.\n  * Construct and maintain automated data processes.\n  * Produce and track business KPIs.\n  * Carrying out data audits.\n\n\n## Data Scientist\nThe end goal of Data Science is to **generate business insights** from data: **current data is used to discover opportunities**. Thus, data scientists are expected to have a good understanding of the challenges a business faces and be able to offer solutions based on a data-driven approach.\nDue to their interdisciplinary expertise, they are highly likely to deal with all aspects of a project including data acquisition, analysis, and interpretation of different types of data (i.e., structured or unstructured) using tools and techniques derived from machine learning, statistics, and data mining.\n### **Key responsibilities**\n  * Work closely with subject-matter experts (SMEs) to identify issues and use data to propose a solution.\n  * Leverage machine learning tools and various statistical techniques to solve problems.\n  * Data cleaning.\n  * Source data to solve business problems.\n  * Collaboration across several teams such as business teams, engineering teams, and product teams.\n\n\n_**Learning resource**_ _: The Data Scientist with_[ _Python_](https://www.datacamp.com/tracks/data-scientist-with-python) _or_[ _R_](https://app.datacamp.com/learn/career-tracks/data-scientist-with-r) _career tracks on DataCamp is a good starting point._\n## Data Engineer\nData engineers build data pipelines to prepare and transform raw and unstructured data. A pipeline typically consists of the collection (possibly from various sources), processing, and storage of data. Much of their time is spent ensuring these pipelines are robust, reliable, and trustworthy enough to deliver.\nThe end goal of data engineering is to **make data accessible**. In other words, they acquire the commodity that makes likes of data science and machine learning possible: some would go as far as to argue that they are the most important players in a data team.\n### Key responsibilities\n  * Design, develop, and maintain data systems and pipelines.\n  * Data acquisition.\n  * Analyze and organize raw data.\n  * Improve data reliability and quality.\n\n\n_**Learning resource**_ _: The Data Engineering with_[ _Python_](https://app.datacamp.com/learn/career-tracks/data-engineer-with-python) _career track on DataCamp is a good starting point. You may also want to extend on your learning with the_[ _IBM Data Engineer professional_](https://www.coursera.org/professional-certificates/ibm-data-engineer) _certificate on Coursera._\n## Data Architect\n\"_A data architect_ _**designs and builds data models to fulfill the strategic data needs of the business as defined by chief data architects**_ _. At this level, you will: undertake design, support, and provide guidance for the upgrade, management, de-commission, and archive of data in compliance with the data policy._ \" [**Source** : [GOV.UK](https://www.gov.uk/guidance/data-architect)].\n  * Identifying data sources (internal and external) and coming up with a plan for data management\n  * Developing and implementing an overall organizational data strategy.\n  * Collaborating with cross-functional teams and stakeholders to permit smooth functioning of the data system.\n  * Managing end-to-end data architecture.\n  * Auditing data management systems and refining them when necessary.\n\n\n## Machine Learning Engineer\nThe end goal of machine learning engineering is to **convert data into products**. The role arose from the need to bridge the gap between the work of data scientists (i.e., analysis and modeling) and the world of software products (i.e., robust system engineering).\nThus, machine learning engineering is typically considered to be a subfield of software engineering: except for the machine learning requirements, machine learning engineers and software engineers have pretty similar lifestyles - this means they are expected to be proficient programmers that are familiar with tools like IDEs, GitHub, and Docker.\n### Key responsibilities\n  * Designing and building machine learning systems.\n  * Building automated pipelines to deploy machine learning models.\n  * Appropriately testing machine learning systems and monitoring their performance.\n  * Working with the likes of data engineers to build data and model pipelines.\n\n\n_**Learning resource**_ _:_[_How to Become a Machine Learning Engineer_](https://towardsdatascience.com/how-to-become-a-machine-learning-engineer-e420e134c0a3) _can be referenced as a learning track._\n## MLOps Engineer\nMLOps is the new craze on the block that is about applying DevOps principles to machine learning systems. Thus, the focus of an MLOps engineer is usually more on the deployment of machine learning models to production - not building them.\nThey enable the machine learning engineers in a similar fashion to how DevOps enables software engineers: engineers will create the software, and Ops will provide the infrastructure and ensure the software runs reliably. Thus, we can say an MLOps engineer is responsible for all activities that occur when a machine learning model is built.\n### Key responsibilities\n  * Build and maintain MLOps pipeline.\n  * Designing and implementing cloud solutions.\n  * Ensuring machine learning applications are scalable with tools like Docker and Kubernetes.\n\n\nWe'll stop here for now.\n_**Note**_ _: There are several other roles based on data that you'll find at various companies (i.e., data storyteller, machine learning researcher,_[_machine learning scientist_](https://app.datacamp.com/learn/career-tracks/machine-learning-scientist-with-python) _, etc.). I recommend you use job boards such as Linkedin Jobs, Indeed, Glassdoor,_[_DataCamp_](https://www.datacamp.com/data-jobs) _, etc to conduct research._\nThe list of jobs provided is by no means extensive and should only serve as a guide. The reader should take this information and proceed to research the tech stack required for each role and build a portfolio. A key thing to remember is that different companies organize their teams in different ways: Different technical terms may be used to describe the same job at two separate companies.\n_Thanks for reading._\n**Connect with me:** [LinkedIn](https://www.linkedin.com/in/kurtispykes/) [Twitter](https://twitter.com/KurtisPykes) [Instagram](https://www.instagram.com/kurtispykes/)\nIf you enjoy reading stories like this one and wish to support my writing, consider [becoming a Medium member](https://kurtispykes.medium.com/membership). With a $5 a month commitment, you unlock unlimited access to stories on Medium. 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