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We hire from conversations, not postings.

Eastridge Analytics is a graph intelligence firm. We do not keep a running list of open roles, because the work arrives in shapes we cannot forecast a quarter ahead. What we do keep is a shortlist of people we would call first, and it is built entirely from conversations that started before there was a job to fill.

Introduce yourself See the work

What you would be joining.

A consulting practice and two products, built on the same foundations.

We build graph-based systems for enterprises that have run out of road with rows and columns: fraud rings at global banks, duplicate identities across millions of customer records, knowledge graphs in private equity, pandemic-relief oversight for the PRAC. The same foundations run underneath our own products, Argus and Bridgr. Small team, real production systems, and no layer of account managers between you and the person whose problem you are solving.

Graph technology

Neo4j and Cypher as a daily instrument rather than a line on a slide. Modeling entities and relationships around the question that actually needs answering, then making the model survive production load.

Neo4jCypherAPOCGraph Data Science

AI that has to hold up

GraphRAG and retrieval grounded in a real model, so an answer can be traced back to the records it came from. We care more about whether a system can show its work than about how quickly it demos.

GraphRAGRetrievalEvaluationAgents

Data analytics at scale

Entity resolution, fuzzy matching, community detection, centrality and similarity, applied to data that is messy in the ways real data is messy.

Entity resolutionPipelinesPythonStatistics

Software people use every day

Argus and Bridgr are products with real users, not internal tooling. Shipping them means owning a feature end to end, from the data model through to what appears on the screen.

PythonTypeScriptRustLocal-first

Who we want to hear from.

Not an exhaustive list. If you are close, write anyway.

Graph and data engineers

People who have modeled a domain as a graph and then had to defend the model when the data argued back. Pipelines, ingestion and the unglamorous work of making a system dependable.

Data scientists and ML engineers

Comfortable with graph algorithms and with the statistics underneath them, and equally comfortable saying when a simpler method would do the job better.

Product engineers

Builders who want their work in front of users rather than in a backlog. Argus and Bridgr are both small enough that one person can meaningfully change how the product feels.

Consultants who can sit with a client

The rarest combination we look for: enough technical depth to design the system, and enough plain language to explain it to the person paying for it.

People early in their careers

We would rather meet someone sharp and curious three years before we can hire them than never meet them at all. If you are still studying or just out, write anyway.

What we look for.

Judgment over credentialsWe care what you have built and how you decided to build it that way. Degrees and titles tell us very little.

FinishingPrototypes are easy. We value the people who stay through deployment, handover and the week after go-live.

Plain languageIf you can explain a graph model to a general counsel or a fraud investigator, you can do half this job already.

Curiosity about the domainThe interesting part is usually the business, not the algorithm. The best work here comes from people who ask why the data looks the way it does.

Honesty about tradeoffsIncluding the maturity to say when a graph is the wrong tool. We have told clients that, and it is why they come back.

Small-team temperamentLittle process, wide ownership, and the expectation that you will pick up whatever the project needs that week.

No role listed

That is not a reason to wait.

Most of the people we have worked with came to us long before a position existed. A short note with something you have built is worth more here than a polished application to a posting that is not there. We read everything, and we reply.

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