Data Scientist — Modelling, Experimentation and Applied Machine Learning, US Employers

Banks, insurers, health systems, retailers, consumer technology companies and government contractors across the United States hire data scientists to build statistical and machine learning models against their own data. It is one of the fastest-growing occupations the federal government tracks, and one where the job title covers several genuinely different jobs.

What the work involves

Framing a business question as something measurable, building and validating models against it, designing and reading experiments, and getting the result into production where it changes a decision. In practice a large share of the week is data preparation and stakeholder conversation rather than modelling, which is the part the courses do not show.

Requirements

• Python or R for statistical modelling and machine learning, and SQL at a level most candidates underestimate
• Machine learning frameworks, and experience of a model that reached production rather than a notebook
• Cloud analytics platforms for building and scaling, named specifically on most postings
• Business intelligence tools — Power BI, Tableau, Alteryx — on the more analyst-leaning roles
• The ability to explain a result to someone who will act on it and does not want the method
• Domain knowledge in finance, healthcare or the employer's sector, weighted more heavily than candidates expect

How the pay is reported

• The measured figure is the federal median of $112,590 a year for data scientists
• Job board averages run higher — around $131,121 — because they average advertised postings rather than measuring paid wages
• Total compensation figures are a different quantity. Packages quoted above $300,000 are base plus bonus plus equity at a small number of employers, not salaries

Before you apply

Ask which of the several jobs this title covers. Modelling, experimentation, analytics and machine learning engineering are advertised under one heading and screened very differently. And when a number is quoted, establish whether it is base salary or total compensation before you compare it with anything.

Note: pay, equity, remote eligibility and any sponsorship decision are set by each employer — not by JobGader. Confirm the details on the employer's own advertisement before applying.