Starting a Career in Data Science in the Age of AI
It was a really hard question to answer, because I myself entered the workforce almost 20 years ago, and the field of data science 10 years ago, and so much has changed since that time. Questions of ethics were much less pronounced (although not absent) when I became a data scientist, and today they are front and center. Data science broadly, and machine learning engineering in particular, have fantastic applications in pretty much every sector, much more so than in the past, so I encourage students to consider fields like healthcare, government, nonprofits, and other sectors.
- ▪It was a really hard question to answer, because I myself entered the workforce almost 20 years ago, and the field of data science 10 years ago, and so much has changed since that time.
- ▪Questions of ethics were much less pronounced (although not absent) when I became a data scientist, and today they are front and center.
- ▪Data science broadly, and machine learning engineering in particular, have fantastic applications in pretty much every sector, much more so than in the past, so I encourage students to consider fields like healthcare, government, nonprofits
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| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/starting-a-career-in-data-science-in-the-age-of-ai/ |
| Publication time | Fri, 18 Sep 2026 12:30:01 GMT |
| Retrieval time | 2026-09-18T12:33:45.213Z |
| Last seen | 2026-09-18T12:33:45.213Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 4xrqOjTsXtOH · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Data ScienceStarting a Career in Data Science in the Age of AIHow do you set yourself up for a career that will stand the test of time when things are changing so fast?Stephanie KirmerSeptember 18, 20268 min readPhoto by delfi de la Rua on Unsplashworld map with pushpinsI was recently asked by a college student in data science and computer science for my advice on entering and succeeding in the field of data science and machine learning today, without selling your soul or sacrificing your ethics. It was a really hard question to answer, because I myself entered the workforce almost 20 years ago, and the field of data science 10 years ago, and so much has changed since that time.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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