Evaluation Specialist
Wild Me By Conservation X Labs
Evaluation Specialist Consultant
CXL is seeking an Evaluation Specialist consultant to serve as the Fund's Experimental Design Lead. This Fund-level position is independent of all implementing partners and is responsible for the scientific integrity of the Fund's impact evaluation strategy.
About Conservation X Labs Inc
Our mission is to prevent an impending sixth mass extinction – the first in Earth's history driven by the actions of a single species: ours. Unlike traditional conservation efforts, Conservation X Labs focuses on leveraging the best technology, the newest innovation, interdisciplinary genius, and the power of the marketplace to boldly confront the biggest problems facing the planet. Though humans have driven this sixth extinction, we know humans have the power to reverse it.
Description
Conservation X Labs (CXL) seeks to solve the world's greatest conservation problems by supporting innovative solutions that address the underlying drivers of extinction. We develop new technology, lead innovation competitions, and empower talented innovators across disciplines to create transformative products that serve people and our planet.
CXL is the operational lead for a new philanthropic Fund tackling fire — the leading cause of tropical primary forest loss — across three of South America's most globally significant ecoregions: the Amazon, the Pantanal, and the Chiquitania. The Fund brings together a diverse coalition of local, regional, and international partners to build and test integrated fire prevention and response systems, with the goal of learning quickly and scaling what works. Interventions span community-led stewardship, technology and innovation, behavior change, accelerated ecological restoration, and financial mechanisms that incentivize fire-safe land management.
The Role
CXL is seeking an Evaluation Specialist consultant to serve as the Fund's Experimental Design Lead. This Fund-level position is independent of all implementing partners and is responsible for the scientific integrity of the Fund's impact evaluation strategy.
The Evaluation Specialist will validate the evaluation design, lead pre-registration of study protocols, oversee a quasi-experimental impact evaluation across multiple intervention geographies, and lead mixed-methods assessments at mid-term and close. Working closely with the Fund's M&E Expert Lead and scientific research partners, this consultant will ensure the Fund generates credible, publishable evidence about the impact of integrated fire management interventions at landscape scale.
The current program configuration spans multiple sites across three ecoregions and is designed as a rigorous exploratory phase: generating effect size estimates and site-level variability data that will power a formally confirmatory study at full scale. The Evaluation Specialist will help frame and communicate this phased approach while maintaining the highest standards of scientific integrity.
This is a rare opportunity to shape the evaluation architecture of a first-of-its-kind conservation fund — one that combines independent remote sensing, causal inference methods, and community-level data to answer consequential questions about what works in fire management.
Key Activities
- Validate and finalize the experimental design
- Define and lock study site boundaries across all intervention and comparison geographies, in coordination with implementing partners and the M&E Expert Lead
- Pre-register the evaluation design on an appropriate open science platform, including: research questions and hypotheses, study timeline, randomization approach where feasible, primary and secondary outcomes, statistical methods, covariates, missing data handling, minimum detectable effect sizes, and protocol deviation procedures
- Coordinate with remote sensing specialists on the primary outcome variable methodology
- Collaborate with academic and scientific research partners on the application of both established and novel causal inference methods to the Fund's multi-geography dataset
- Conduct periodic spot-checks of partner-reported operational data to assess consistency across geographies and flag credibility risks
- Lead or co-lead the mid-term evaluation (Years 2–3) and the final evaluation, applying a contribution analysis framework to address attribution challenges arising from climate variability and external drivers
- Monitor and document spillover effects from intervention to comparison sites as part of the evaluation protocol
- Produce peer-reviewed or publication-ready findings that assess findings, draw inferences, and synthesize results across the Amazon, Pantanal, and Chiquitania ecosystems
- Advise on study design expansion and sample size requirements if the Fund scales to additional geographies
Deliverables
- Validated experimental design document, including locked site boundaries and pre-registration package, submitted to an open science registry
- In accordance with the experimental design, and in collaboration with the M&E and technical leads, develop data collection templates that are culturally adapted to the local context (language, culture, etc) and standardized across the different LIFTs.
- Year 1 variability and statistical power analysis report, with recommendations for Phase 2 study design
- Outcome variable methodology documentation, prepared in collaboration with remote sensing partners
- Periodic partner-reported data spot-check reports
- Mid-term evaluation report (Years 2–3), applying contribution analysis and documenting spillover
- Final evaluation report
- Publication-ready manuscript(s) or policy brief(s) summarizing causal findings across geographies
- Phase 2 study design recommendations, scoped for program expansion
Qualifications
- Advanced degree (PhD preferred) in evaluation, statistics, economics, ecology, or a related quantitative field
- Minimum 7 years of experience designing and leading impact evaluations or quasi-experimental studies in conservation, natural resource management, or international development
- Demonstrated expertise in quasi-experimental designs, such as before-after control impact (BACI), difference-in-differences, or statistical matching
- Experience pre-registering evaluation designs on open science platforms (e.g., OSF, RIDIE, or equivalent)
- Familiarity with remote sensing data and satellite-based monitoring in tropical forest or fire management contexts is preferred
- Knowledge of advanced causal inference methods (e.g., double machine learning, synthetic control) is a strong advantage
- Experience working independently of implementing partners, with a demonstrated ability to serve as a credible, arms-length evaluator
- Experience producing peer-reviewed publications or policy-relevant outputs from program evaluation data is preferred
- Fluency in English, Spanish, and Portuguese required
Preferred:
- Familiarity with fire, land use, or tropical forest conservation
Please note: To apply you must submit: A brief cover letter (no more than two pages) describing your experience with impact evaluation design and your approach to causal inference in complex, multi-site conservation programs
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