Articles / Skill clusters, explained — like Google Maps for careers
Skill clusters, explained
Job boards count. Tevos charts.
Job boards count. They tell you there are 50,000 results. Tevos charts. We tell you which 200 are in your neighbourhood, and which one is a metro stop away if you learn one new skill.
Open Google Maps. Type "Bangalore." That same map exists for your career — we just built it.
The map you've been driving without
For thirty years, every job platform has handed you a list. Recency-sorted, keyword-filtered, endless. The interface hasn't changed since Monster.com in 1996, and neither has the question it answers: which jobs match these strings I typed?
That's the wrong question. The right one is the question you'd ask Google Maps if you were moving to a new city: where am I, where could I go, and what's the route between? It's a map question. It needs a map answer.
Lists can't give you one. So we drew the map.
Six neighbourhoods around your CV. Click a cluster to isolate it. The dashed lines are the bridge skills — the metro between zones.
That's a slice of the live map. Six neighbourhoods, one CV at the centre, dashed lines for the metro routes between them. Real clusters extracted from 380,000 active job descriptions — none of them predefined. Data Engineering is a cluster because 60% of data eng JDs mention Python and SQL together, but only 5% of all JDs do. The lift is real. Whitefield is a tech corridor for the same reason: people work there, eat there, live nearby, and the data reflects it.
Job boards count. Tevos charts.
Why distance matters
Three things change the moment you can see the map.
Distance is real. "Should I learn Rust?" stops being a yes/no — it becomes where am I, and how far is Rust from here? If you live in the systems cluster, Rust is two metro stops away. If you live in SAP enterprise, it's a different continent. Same skill, different map, different answer.
Cluster-spanning JDs are tells. Ever seen "5+ years React, plus Kafka, plus SAP, plus Power BI" and thought nobody has all four? You're right. That JD bridges three clusters that don't share territory. It pays more, takes longer to fill, and we flag it. Useful information when you're picking battles.
Bridge skills are the metro lines. SQL connects data, backend, and analytics. Python connects ML, automation, and web. These aren't generic skills — they're the trains between neighbourhoods, and we surface the ones your local network needs before your current cluster cools.
The map redraws itself
Bangalore in 2010 looked nothing like Bangalore in 2026. Whitefield used to be the city's edge; now it's a node. The skill map shifts the same way — the AI cluster today is LangChain, vector databases, retrieval pipelines. Five years ago it was applied ML with TensorFlow at the centre. Five years before that, data mining.
We rebuild the cluster graph weekly. We don't pretend the map is fixed. We just show you the latest version, where you stand on it, and which neighbourhoods are growing, stalling, or got rezoned overnight.
Lists vs routes
The difference isn't subtle. It's the entire shape of the answer.
// every other job board
"Does this JD contain Python?"
50,000 results. Sort by recency. Apply to 200. Hear back from 4. The interface is a list because keyword search is the only question the system can answer. The reason it feels exhausting is that it is exhausting — by design.
// tevos
"Is this JD in your neighbourhood?"
200 results, sorted by how naturally each one fits your trajectory. Each labelled inside your cluster, adjacent, or across town. The metro hop spelled out before you click apply. Lists count what's there. Maps tell you where to go.
Lists are how job boards keep you scrolling. Maps are how you arrive.
Why the map doesn't lie
A map is only useful if it's honest. Three constraints keep the cluster graph from drifting into nonsense — and they're the reason a competitor can't replicate this in a sprint.
90d
freshness floor
JDs expire at 90 days unless they're confirmed live. A job that closed six months ago tells us nothing about today's market. We don't ask it to.
k≥12
edge floor
No cluster edge counts unless 12 distinct JDs back it. The long tail of "one weird recruiter wrote one weird JD" gets filtered out before it touches the graph.
3B
on our metal
Skill labels are extracted by a Qwen 2.5-3B model running on our server. Not a vendor. Not a third-party preprocessor. The raw signal stays ours, and so does the moat.
The full pipeline lives at methodology.php. The corpus the map is drawn from is summarised at stats.php. The industry-sliced version of the same map is at industries.php. Audit whichever one your gut needs to read first.
Get on the map
If you're reading this and wondering whether to learn Rust, switch from data eng to ML, or hold onto Java for one more year — that's the question this map answers. Not in a 30-minute call. In six minutes, free, no card required.
Stop reading job ads. Start reading the map.