Hire an AI spatial data scientist

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• The question written as a testable statement
• A first look plan, including a test for spatial dependence
• A method choice with the reason and a runner-up
• Numbered analysis steps and checks for bias and uncertainty
• Draft code in Python or R, marked as not run
• A way to explain the result with a map, the numbers and one plain sentence

How it does the job well

• Never state a result, p-value, coefficient or cluster you did not get from the user's own output. You cannot run code or see data.
• If the user pastes results, read them and say what they do and do not show. Do not say a model is good without the checks above.
• Do not present exploring as confirming. Say which one the work is.
• Package functions and versions change. Mark specific calls "check the package docs".
• Give drafts and proposals. Get the user's go-ahead before anything that sends, publishes, spends money or changes a live system.
• Agree the question and the decision it supports before analysing.
• Check the data first: date range, grain, missing values, duplicates and definitions.

Mistakes it avoids

• Reporting a change without checking whether tracking or definitions changed.
• Treating correlation as cause.
• Averages that hide important segments or outliers.

What a good result looks like

An example, not your task: You think complaints, sales or incidents cluster in places and want to test it.

• Question and assumptions. The question as one testable statement, with assumptions and MISSING items.
• Data and first look. Checks to run before modeling, including a test for spatial dependence.
• Method choice. The method, why it fits, its key assumption and a runner-up.
• Analysis steps. Numbered steps with settings and the output of each.
• Checks. Boundary sensitivity, leftovers check, cross validation and uncertainty, each with a pass line.
• Draft code. Python or R code for the main steps, marked as not run.

How the work is checked

• Numbers reconcile to the source totals, and the reconciliation is shown.
• Every chart and number states its source, period and definition.
• Limits and next checks are listed.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Frame the question. It turns your question into one testable statement and marks missing facts as MISSING.
• Plan the first look. It plans maps, charts and a test for spatial dependence before any model.
• Choose the method. It picks a method that fits the question and the data, with a reason and a runner-up.
• Plan the model and checks. It plans the fit, then the leftovers check, the boundary sensitivity test and cross validation.
• Plan the uncertainty and records. It plans error ranges, fixed random seeds and a log of every data change.
• Plan the explanation. It plans a map, the key numbers and a plain sentence, and warns against reading cause from pattern.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id gis-spatial-data-scientist only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/gis-spatial-data-scientist/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• The location question in one sentence, such as where are late deliveries clustered
• Your data: what each row is, the measure, the time span and how many rows
• The area units, such as points, postcodes, tracts or a grid
• Your tools, such as Python with GeoPandas, R, QGIS or ArcGIS Pro (optional)
• Whether this is exploring or testing a set idea, and who will read it
• Your own results to read, if you have run something (optional)

Helpful context (optional)

• The column names and first ten rows of your data.
• A map of the zone boundaries you use.

Tools and access

• Claude, ChatGPT or Gemini: The plan and draft code work from a pasted profile and your data description.
• Claude Code: It can read your data files and run Python in your project if you allow it, so you can check results.

Functions and deliverables

• Spatial data scientist: Plan a spatial analysis, such as hot spots, clusters or location based prediction, with the right test, bias checks and plain language results.
  • The question written as a testable statement
  • A first look plan, including a test for spatial dependence
  • A method choice with the reason and a runner-up
  • Numbered analysis steps and checks for bias and uncertainty
  • Draft code in Python or R, marked as not run
  • A way to explain the result with a map, the numbers and one plain sentence
  Source: Michael Sitarzewski and Agency Agents contributors.
• Qdrant cluster capacity planner: Get a Qdrant scaling plan: how much RAM or how many nodes you need, in what order to add them, and what to measure first.
  • The bottleneck, named, with the numbers that show it
  • A scaling order, such as a bigger machine first and more machines later
  • A rough memory estimate from your data size, marked as an estimate
  • Node and copy counts to start with, if more machines are needed
  • The first test to run and what result to look for
  Source: GitHub Copilot community.
• Language model feature explorer: Use SAELens to break a model’s inner signals into sparse features. Get code to train, inspect and test them.
  • Training code for the feature network
  • Named features with example text
  • Charts of which features fire
  • Steering and removal tests
  • Notes on quality checks
  Source: Orchestra Research.
• Analysis write-up builder: Share your finished analysis. Get a write-up with the question, data, method, results and steps to rerun it.
  • A complete analysis document
  • Data source and method sections
  • Results with captioned charts
  • Recommendations and rerun steps
  Source: Nimrod Fisher.
• Metric definition writer: Share a metric, calculation and business use. Get a YAML definition, validation results and questions for the data owner.
  • A filled metric, dimension or entity YAML
  • A validation report
  • dbt-ready field names
  • Notes on edge cases
  Source: Nimrod Fisher.

Tasks you can use it for

• You think complaints, sales or incidents cluster in places and want to test it.
• You want to model prices, risk or demand using location, such as retail site choice.
• A regression on area data gives odd results and you suspect neighbors affect each other.
• Your result changes when you change the zone boundaries, and you need to test how much.

Limits to check before starting

• You only need a clear map or a simple query: use the GIS analyst.
• You need to detect objects or land cover in images with machine learning: use the Geospatial machine learning planner.
• Your data is messy and needs cleaning or joining first: use the Spatial Data Engineer agent.
• Your data has no location and you need general statistics: use the Statistician agent.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Add the pack to your AI as a skill. Or open a new chat and paste SKILL.md, then references/source-agent.md.
• Write the question, then describe your data. List the columns, the unit of area and the row count. Leave out private details.
• Say 'Plan this spatial analysis. Do not guess my data.' Your AI asks once for what is missing.
• Read the method choice and the checks first. Say if you must use a tool you already have.
• Run the draft code on your data. The AI cannot. Paste the results back and ask it to help you read them.
• Have a qualified analyst review the work before you publish figures or make big decisions.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/gis-spatial-data-scientist for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.