Keyword matching works on two levels: the mechanical ATS match against a job description, and the human reviewer's six-to-eight-second scan for "does this person actually know the tools we use." Both reward the same thing — specific, current, correctly used terminology — and both penalize generic filler like "team player" or "detail-oriented" that carries no field-specific signal.
Software Engineering
- —Languages and frameworks actually used, versioned where it matters (e.g. 'React 19', not just 'React')
- —System design terms tied to real work: distributed systems, microservices, event-driven architecture, API design
- —Infrastructure: CI/CD, containerization (Docker, Kubernetes), cloud platform (AWS/GCP/Azure) with the specific services used
- —Testing discipline: unit testing, integration testing, specific frameworks (Jest, pytest)
Data Science
- —Core stack: Python, SQL, and the specific ML libraries used (scikit-learn, PyTorch, TensorFlow)
- —Methodology terms: A/B testing, statistical significance, feature engineering, model evaluation metrics
- —Data infrastructure: data pipelines, warehousing (Snowflake, BigQuery), orchestration (Airflow)
- —Outcome framing: business metric impacted, not just model type used
DevOps / SRE
- —Infrastructure as code: Terraform, CloudFormation, Ansible
- —Observability: Prometheus, Grafana, Datadog, on-call and incident response experience
- —Reliability metrics: uptime percentage, MTTR, deployment frequency
- —Cloud-native: Kubernetes, service mesh, autoscaling
Product Management
- —Process: roadmapping, prioritization frameworks (RICE, MoSCoW), agile/scrum ceremonies
- —Cross-functional signals: worked with engineering, design, and data teams — named explicitly, not implied
- —Outcome metrics: adoption rate, retention, revenue impact tied to a specific launch
- —Discovery: user research, A/B testing, customer interviews
Data Analysis
- —Tools: SQL, Excel/Sheets at an advanced level, BI tools (Tableau, Looker, Power BI)
- —Statistical literacy: hypothesis testing, regression, cohort analysis
- —Business framing: which decision your analysis changed, not just which chart you built
- —Data quality and pipeline awareness, even in an analyst-not-engineer role
Using these without stuffing
The test for whether a keyword belongs on your resume: could you speak to it, unprompted, for thirty seconds in an interview? If yes, it belongs — ideally inside a bullet point that also states an outcome, not in a standalone list at the bottom of the page. If no, adding it is a liability, not an asset — it passes a keyword scan and fails the conversation two steps later.
Check your own keyword coverage
Career Copilot's AI resume score compares your resume against the keyword profile of your actual target role, not a generic checklist — upload it free and see exactly what's missing for the specific jobs you're applying to.