Echo Labs - Chief Science Officer

Echo Labs is building the scientific and technical foundation for ecological intelligence: a multimodal system to measure, model, and forecast Ecosystem Condition as a dynamic property. We are a collaborative and interdisciplinary team of scientists and engineers engaged in a planetary moonshot – with a public good mission, operating like a start-up. We are a new Focused Research Organization (FRO) supported by Convergent Research and funded by the Advanced Research and Invention Agency to pursue high-risk, high-reward science in the public interest.

The Chief Science Officer (CSO) anchors this effort in scientific rigour, ecological credibility, and long-term legitimacy and plays a decisive role in ensuring that this infrastructure is scientifically sound and transformative. The CSO is responsible for ensuring that Echo’s data, models, and interpretations faithfully reflect state-of-the-art ecological science while enabling bold, operational innovation. This role bridges fundamental ecology, field methods, and applied modelling, translating ecological theory into scalable, decision-grade infrastructure. This is a high-leverage role: you’ll lead Echo’s ecology vertical and relationship with the ecology community. The CSO reports to the CEO and works in close partnership with the Chief Technology Officer and Director of Product & Partnerships as a core member of Echo’s executive leadership team.

Core Responsibilities

• Conceptual Framework: Own and continuously refine Echo’s scientific framing of Ecosystem Condition, grounded in ECT+/SEEA typologies and current ecological theory. Ensure scientific defensibility, interpretability, and transparency of Echo’s outputs for the academic community, policymakers, and external partners. Set standards for scientific validation, benchmarking, and uncertainty characterisation across the programme.
• Data Strategy and Analytical Direction: Lead the identification, evaluation, and strategic use of existing ecological and Earth-observation datasets, determining when external data can accelerate progress versus when new data generation is essential. Set the analytical direction for how existing datasets are integrated, stress-tested, or rejected, including defining appropriate benchmarks, comparators, and limitations. Guide early in-silico analyses to inform sampling design, modelling priorities, and go/no-go decisions, ensuring resources are focused on data and approaches with the highest scientific leverage. Establish criteria for reuse, interoperability, and alignment of legacy datasets with Echo’s Ecosystem Condition framework, avoiding dependence on datasets that are ecologically misaligned or methodologically brittle.
• Ecological Data Generation: Lead scientific oversight of Echo’s sampling architecture, ensuring ecological sensitivity and robustness. Guide methodological choices across in-situ data streams (eDNA, acoustics, vegetation structure, soil health, hydrology, etc.). Oversee ecological QA/QC, signal separability testing, and temporal sensitivity to disturbance and recovery.
• Modelling Integration and Ecological Interpretability: Work hand-in-hand with the CTO and ML leads to ensure ecological meaning is preserved in latent representations (“ecosystem fingerprints”) and predictive models. Validate that modelling choices align with ecological processes, scales, and constraints rather than spurious correlations. Champion explainability and ecological interpretability of model outputs.
• Scientific Leadership, Governance and External Engagement: Act as Echo’s senior scientific representative to the ecological research community. Build trust and adoption through workshops, advisory engagement, and transparent publication of methods, data, and results. Shape Echo’s contribution to emerging standards in ecological monitoring, modelling, and data governance. Interface with the Science Advisory Board, translating feedback into operational scientific decisions.

Progression. In six months you will have...

• Established an internal scientific definition of Ecosystem Condition, aligned with ECT+/SEEA and explicitly scoped to what Echo will and will not measure.
• Audited existing ecological data to identify usable signals, inferable dynamics, and critical gaps, guiding early go/no-go decisions on new data collection.
• Set clear priorities for early models and metrics, focusing on a small number of high-leverage downstream tasks rather than solving everything at once.
• Designed an initial metric and model architecture, including decisions on single vs. multi-layer representations and a strategy for ecological interpretability.
• Led internal and external scientific workshops to align sampling, modelling, and core scientific questions, resulting in shared buy-in and a v1 sampling design.
• Produced foundational scientific artifacts, including a v1 Ecosystem Condition framework, data-to-signal mapping, interpretability plan, and a scientific risk register.

We Offer (Amazing Benefits)

• A rare chance to build institutional architecture for frontier science and work with some of the smartest and most talented experts from different fields.
• Up to a 6% employer pension contribution, with optional additional salary sacrifice.
• Top-tier private medical and dental insurance—fully covered for you and your dependents.
• Generous parental leave—20 weeks make-whole-pay for both parents.
• 25 days of holiday allowance.
• Wellness allowance for fitness and wellness activities.

Profile (You Are)

• PhD (or equivalent) in ecology, ecosystem science, environmental science, or a closely related field.
• Deep understanding of ecosystem functioning, resilience, or biodiversity dynamics, ideally across multiple spatial scales.
• Familiarity with ecological modelling, including interaction with machine learning.
• Strong basis in advanced statistical methods.
• Proven background in rigorous experimental design.
• Demonstrated experience designing, interpreting, or overseeing field-based ecological data collection.
• Track record of interdisciplinary collaboration across ecology, data science, and engineering.
• Exceptional scientific judgement and communication skills, with the ability to translate complexity into clarity.
• Highly organised and outcome-focused, with experience structuring workplans, prioritising trade-offs, and driving progress across interdisciplinary teams under time and resource constraints.

Highly Valued Experience

• Experience working with multimodal ecological datasets (e.g. combining field data with Earth observation).
• Prior involvement in large-scale or national ecological monitoring programmes.
• Experience engaging with policy, regulatory, or applied decision-making contexts.
• Familiarity with FAIR + CARE data principles or ecological data governance.
• Leadership roles in collaborative or mission-driven research initiatives.
• Demonstrated ability to translate scientific vision into delivered products or platforms, with experience taking complex research programmes from concept through execution against clear timelines and milestones.
• Track record of operating in delivery-oriented environments (e.g. research infrastructure builds, applied science programmes, or mission-driven organisations).

We’re bringing together top talent from academia, industry, and startups to build a new model for innovative R&D. We are committed to creating an inclusive and diverse workplace where everyone has the opportunity to thrive. We believe in hiring individuals based on their unique talents—not on race, colour, religion, ethnicity, gender, gender identity, sexual orientation, disability, age, military or veteran status, or any other characteristic protected by law or our company policies. Our goal is to foster a healthy, safe, and respectful environment where all employees are valued and treated with dignity.