--- title: "Are Data Science Jobs in Demand?" description: "Are data science jobs in demand? Yes, spread across data scientist, ML engineer and analytics engineer titles, and selective about engineering skills." canonical: https://jobspipe.dev/blog/are-data-science-jobs-in-demand date_published: 2026-09-09 date_modified: 2026-09-09 author: Dvir Atias --- # Are Data Science Jobs in Demand? Are data science jobs in demand? Yes, spread across data scientist, ML engineer and analytics engineer titles, and selective about engineering skills. Are data science jobs in demand? Yes, though the demand is spread across several titles and has become more selective. Employers post data scientist roles across finance, healthcare, retail and software, but many now ask for engineering skills alongside modelling, and the same work is often advertised as machine learning engineer or analytics engineer instead. ## How to tell whether data science jobs are in demand 1. **Count the titles together.** Data scientist, machine learning engineer, applied scientist, analytics engineer and data analyst overlap heavily. A query on one title undercounts; run them as a set with `job_title_or` and read the mix. 2. **Read the skills asked for.** Python and SQL are the constants; cloud platforms, Spark and the ML frameworks appear on a narrower band of postings. In the published Engineering cut, only Python, AWS and SQL from the data science toolkit make the top 25 skills, which itself says how the function is weighted toward general software and hardware roles. 3. **Separate seniority.** Filter with `job_seniority_or`. Data science postings skew toward mid and senior levels, and an entry-level share that is thin relative to the whole is the main reason candidates feel the market is tight while employers still report open roles. 4. **Split by industry.** Add `isic_division_or`. Demand outside software, in banks, insurers, hospital systems and retailers, is easy to miss when the search starts from technology companies. 5. **Check freshness and closure.** Set `posted_at_max_age_days` and `max_ghost_score`, then look at how quickly the postings close. A title that is posted often but closes slowly is weaker evidence than a smaller set of fresh postings that fill within a month. 6. **Compare with analyst roles.** Data analyst postings are more numerous and ask for less, which makes them a more crowded entry point; see [are data analysts in demand](https://jobspipe.dev/answers/are-data-analysts-in-demand). The honest caveat is that demand for the title and demand for the work are not the same measure. Some employers fold data science into product analytics teams, others advertise the same work as machine learning engineering once it moves from notebooks into production systems, and some large companies hire for the skills under plain engineering titles with no data science group at all. Read a title-only search as a measure of the label and a skills search as a measure of the work. Here is the share of active Engineering postings naming each data science skill: - Python19.2% - AWS11.5% - SQL8.7% Share of active Engineering postings naming the skill. Only skills inside the published top 25 for the function are shown. How this was measured Measured from active postings on the ten collected boards within the named job function; share = postings in that function naming the skill. Generated 2026-09-09. Shares only: JobsPipe publishes percentages, not counts. Skills are matched against a curated lexicon, so one posting can name several. The Engineering function spans software, hardware and industrial roles, which dilutes the data science share; a title-filtered query gives a sharper cut. Shares are cross-sectional and do not support trend claims on their own. ``` curl -X POST https://api.jobspipe.dev/v1/jobs/search \ -H "Authorization: Bearer $JOBSPIPE_API_KEY" \ -H "Content-Type: application/json" \ -d '{"job_title_or":["data scientist","machine learning engineer"],"skills_or":["python"],"posted_at_max_age_days":30,"limit":100}' ``` The full ranking per function is in [in-demand skills](https://jobspipe.dev/blog/in-demand-skills), and the occupation shares that put data roles in context with the rest of hiring are on the [Labour Market Pulse](https://jobspipe.dev/pulse). Try it without a key ``` curl -d '{"limit":3}' https://api.jobspipe.dev/v1/sandbox/jobs/search ``` Only `/v1/sandbox/*` needs no key. ## Where JobsPipe fits JobsPipe is a jobs data API that collects live postings from LinkedIn, Indeed, Y Combinator, Workday, Greenhouse, Workable, SmartRecruiters, Ashby, Lever and Paylocity, returns them as one schema with closure tracking and a ghost score, and includes a free tier of 1,000 jobs to start. The measurement above is one request, and dividing its result by the same query without the title filter turns it into a share: ``` curl -X POST https://api.jobspipe.dev/v1/jobs/search \ -H "Authorization: Bearer $JOBSPIPE_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "job_title_or": ["data scientist", "machine learning engineer", "applied scientist"], "posted_at_max_age_days": 30, "max_ghost_score": 40, "limit": 100 }' ``` ## Frequently Asked Questions ### What skills do data science postings ask for? Python and SQL almost always, then statistics or modelling, a cloud platform such as AWS, and increasingly engineering skills: data pipelines, version control and deployment. Spark and the ML frameworks appear on a narrower set of postings. Query skills\_or or read technology\_slugs on each returned row to measure a segment yourself. ### Is data science harder to get into than data analysis? Generally yes. Data analyst postings are more numerous, ask for SQL and a spreadsheet or BI tool, and include more entry-level roles. Data science postings skew toward mid and senior levels and expect modelling plus engineering skills. Many people enter through an analyst role and move across once they have production experience. ### How do I measure data science demand with the API? Call POST /v1/jobs/search with job\_title\_or set to data scientist, machine learning engineer and applied scientist, bound the window with posted\_at\_max\_age\_days, and filter stale reposts with max\_ghost\_score. Divide the result by the same query without the title filter to get a share, and split by job\_seniority\_or or isic\_division\_or for level and industry. Related ## Keep reading - [Guide · Sep 9, 2026Are Data Jobs in Demand?](https://jobspipe.dev/blog/are-data-jobs-in-demand) - [Guide · Sep 9, 2026How to spot ghost jobs: a seven-point checklist mapped to real data fields](https://jobspipe.dev/blog/how-to-spot-ghost-jobs) - [Guide · Sep 9, 2026LinkedIn ghost jobs: how common they are, what they look like in the data, and how to filter them out](https://jobspipe.dev/blog/linkedin-ghost-jobs) - [Guide · Sep 9, 2026Why do companies post ghost jobs? The stated reasons, the accidental ones, and what the data shows](https://jobspipe.dev/blog/why-do-companies-post-ghost-jobs) - [Changelog · Apr 4, 2026Webhooks GA, plus 12-month historical jobs data on Pro plans](https://jobspipe.dev/blog/webhooks-ga-12mo-history) ## Further reading - [What does in demand mean?](https://jobspipe.dev/answers/what-does-in-demand-mean) --- Try it with no key: `curl -d '{"limit":3}' https://api.jobspipe.dev/v1/sandbox/jobs/search`. AI agents: the full machine-readable index of this site is https://jobspipe.dev/llms.txt?src=md-twin - API quickstart, MCP server, pricing. Free key (1,000 jobs to start): https://jobspipe.dev/signup