Machine Learning
Which roles require it and how much it matters — mapped across real job-role profiles.
Short answer: Machine Learning is asked for by 3 of the 103 job-role profiles Skilture maps, and is a core requirement in 3 — most heavily by Bioinformatics Scientist, Data Scientist and Machine Learning Engineer.
Source: Skilture's 103 job-role profiles · Updated
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- Roles that list it
- 3 of 103
- Core requirement
- 3 rolesnon-negotiable there
- Skill type
- Technicalhard, teachable
Who hires for Machine Learning?
The roles in Skilture's catalogue that list Machine Learning, and how heavily each one weights it.
Bioinformatics Scientist
Required as a hard skill.
Data Scientist
Required as a hard skill.
Machine Learning Engineer
Required as a hard skill.
Targeting one of these roles? Grab the ATS resume keywords:
How interviewers test Machine Learning
Real questions from Skilture's interview banks, with what the interviewer is listening for.
Bioinformatics Scientist · basic
What is the role of a confusion matrix in evaluating a machine learning model, particularly in a classification task?
Listen for: The candidate should explain how a confusion matrix visualizes the performance of a classification algorithm by showing true positives, true negatives, false positives, and false negatives. They should also mention how metrics like accuracy, precision, recall, and F1-score are derived from it. A red flag is not understanding these fundamental terms.
Bioinformatics Scientist · basic
What is the difference between supervised and unsupervised machine learning? Provide a bioinformatics example for each.
Listen for: The candidate should clearly define supervised learning as using labeled data to predict outcomes (e.g., classifying disease vs. healthy based on gene expression) and unsupervised learning as finding patterns in unlabeled data (e.g., clustering samples into subtypes based on gene expression). A red flag is confusing the two or providing incorrect examples.
Data Scientist · intermediate
Explain the bias-variance trade-off and how it shows up in practice.
Listen for: Look for underfitting (high bias) versus overfitting (high variance), and levers like model complexity and regularisation. Reciting the phrase without connecting it to overfitting is a red flag.
What roles that use Machine Learning pay in the US
Annual wages in US dollars for the roles above: median, and the 10th–90th percentile range.
| Role | Median | Range |
|---|---|---|
| Machine Learning Engineer | $135,980 | $82,460 – $214,670 |
| Data Scientist | $120,230 | $67,240 – $199,130 |
United States, USD. Source: O*NET OnLine (BLS Occupational Employment and Wage Statistics), 2025; each median links to its occupation. India (INR): no sourced figure for these roles yet, so none is shown.
Skills that pair with Machine Learning
Employers hiring for Machine Learning most often ask for these alongside it.
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Machine Learning: common questions
- Is Machine Learning worth learning in 2026?
- Machine Learning is listed in 3 of the 103 job-role profiles Skilture maps, and it is a core requirement in 3 of them. Explore the roles above to see where it matters most, then run a free skill gap analysis to see if you already have it.
- Which jobs require Machine Learning?
- Roles that ask for Machine Learning include Bioinformatics Scientist, Data Scientist, Machine Learning Engineer. You can check how your own skills match any of these roles with Skilture's free skill gap analysis — no signup required.
- How do interviewers test Machine Learning?
- A typical question, from Bioinformatics Scientist interviews: "What is the role of a confusion matrix in evaluating a machine learning model, particularly in a classification task?" What the interviewer listens for: The candidate should explain how a confusion matrix visualizes the performance of a classification algorithm by showing true positives, true negatives, false positives, and false negatives. They should also mention how metrics like accuracy, precision, recall, and F1-score are derived from it. A red flag is not understanding these fundamental terms.
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