Leading a team of analysts conducting real - time surveillance of overnight equity trading. Ensuring compliance with regulatory requirements and maintaining market integrity during extended trading hours.
About the role
- Director of Modelling at Echo Labs, designing data pipelines for ecological intelligence and leading interdisciplinary research. Collaborating with executives to translate ecological data into actionable insights.
Responsibilities
- Design experiments that isolate signal from noise in ecological data, and design tests that can increment progress forward when results are ambiguous.
- Work with the CTO to prioritize intermediate outputs, publication opportunities, and risks.
- Work iteratively with the CSO to test and refine modelling approaches and uncertainty quantification that can be interpreted in ecological context.
- Define a standardization strategy that ensures model comparability across ecosystems and geographies.
- Translate ecological hypotheses from the CSO into testable ML experiments.
- Implement, train, and evaluate models that characterise ecosystem condition across multiple measurement modalities.
- Contribute to research outputs: white papers, technical documentation, and peer-reviewed publications.
- Working with a growing team under your management, you will develop and maintain the technical stack to ensure reproducible, rapid experimentation.
- Work with the leadership team to hire and lead additional engineers and data scientists.
- Evaluate and communicate needs and opportunities to creatively accelerate progress.
- Establish technical standards, code review practices, and documentation norms.
Requirements
- 8+ years in machine learning, data engineering, or applied research with end-to-end system ownership.
- Track record with machine learning infrastructure and experiment tracking, model versioning, reproducible pipelines.
- Experience with multimodal data. At minimum two of time-series sensor data, audio/acoustic data, imagery, or geospatial/remote sensing.
- Experience with cloud platforms (AWS or GCP) and MLOps tooling.
- Demonstrated ability to scope problems, make architectural decisions, and deliver without close supervision.
- Strong scientific communication. You have the ability to explain technical choices across domains and document work for reproducibility.
Benefits
- Performance-based bonus
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