Operational data and machine learning for environmental research teams

Make your data usable. Make your models reliable.

We help environmental and conservation teams turn messy field data into dependable, reproducible workflows— so you spend less time wrangling and more time doing science.

Confidence Safety Traceability Reproducibility Knowledge transfer
Remote-first. Fixed-scope deliverables. Clear handoffs. Built for small teams with big datasets.
Offerings
Start small. Scale when it’s working.
Rescue Audit
2 weeks
A fixed-scope engagement to map your workflow, identify failure points, and produce a practical 30/60/90 plan.
  • Current-state map + bottlenecks
  • Priority backlog + risk register
  • Funder-ready summary text
Onboarding Sprint
2–4 weeks
Bring a dataset online: ingestion, standards, QA, and handoff. Ideal for new populations, sensors, or partnerships.
  • Ingest + normalize metadata
  • QA report + known limits
  • Reproducible pipeline + docs
Model Readiness & Evaluation
1–2 weeks
A clear answer to “is this model working?” and “what do we do next?” without committing to months of training.
  • Evaluation protocol + metrics
  • Error analysis + failure modes
  • Data strategy + next steps
Longer engagements are available for teams ready to operationalize end-to-end workflows: data pipelines, reproducibility, cloud cost controls, monitoring, and deployment hygiene.
Who this is for
If any of this sounds familiar, we should talk.
Teams
Small research groups, NGOs, labs, and field teams with growing datasets and limited engineering bandwidth.
  • Population monitoring programs
  • Bioacoustics + photo-ID projects
  • Multi-partner data collaborations
Typical problems
Workflows that “sort of work” until the dataset grows, staff changes, or the grant ends.
  • Manual QA and messy metadata
  • Stalled ML initiatives
  • Unclear provenance / reproducibility
What you get
Concrete artifacts you can operate and fund—not just advice.
  • 30/60/90 plan + backlog
  • Risk register + mitigations
  • Runbooks + handoff docs
Trust & safety
Operational hygiene that makes partnerships and funders more comfortable.
Data handling
We default to working inside your existing accounts and permissions, with least-privilege access.
  • NDA-friendly
  • No data export without approval
  • Audit trails where possible
Operational handoff
Every engagement ends with written docs and a clear operating procedure your team can run.
  • Runbooks + checklists
  • Monitoring / alerting recommendations
  • Reproducibility guardrails
About
Pragmatic, field-aware engineering
Operational Ecology is a small consultancy focused on environmental data systems and machine learning. We build “boring” infrastructure on purpose: workflows that are safe, auditable, and maintainable, with clear documentation and handoff.

Work is led by an applied machine learning engineer and researcher in conservation-focused computer vision and ecological data pipelines—experienced with long-term datasets, photo-ID workflows, and operational systems that must survive beyond a single grant cycle.
Clear scope Written deliverables Secure access patterns Handoff docs No heroics required
Approach
Trust by design
  • Confidence
    Define metrics, measure outcomes, and make reliability visible.
  • Safety
    Least-privilege access, careful data handling, and audit trails.
  • Knowledge
    Documentation, runbooks, and training so your team can operate independently.
Start
Two-week rescue audit
  • You get
    A current-state map, a prioritized 30/60/90 plan, and a funder-ready summary.
  • Best for
    Teams with growing datasets, manual workflows, or stalled ML initiatives.
  • How it starts
    A short call → access to existing docs/systems → a clear, written output.
Ask about the rescue audit
Nonprofit-friendly scope and pricing. Fixed-fee deposit/milestone terms available on request.
© Operational Ecology hello@operationalecology.io