Title: Machine Learning Applied Scientist (Founding Team)
Location: NYC or London (Hybrid/Remote flexibility)
Our client is a well funded, research-driven private equity firm focused on transforming the financing of natural resource projects—starting with mining. With $2.2M raised from Village Global at the idea stage, the team is pioneering a new model that combines deep domain expertise in Earth sciences with cutting-edge AI/ML capabilities.
The opportunity is massive: the natural resources market lacks institutional risk capital and digital innovation. This company is poised to become the KKR or Blackstone for physical asset investing—leveraging AI, satellite imagery, economic models, and geological data to identify and underwrite high-value opportunities globally.
As the first Machine Learning Applied Scientist on the founding team, you'll serve as the technical backbone for our data and modeling capabilities. You'll work closely with geoscientists, investment professionals, and software engineers to architect, prototype, and deploy models that help us evaluate natural resource projects at a global scale.
This role blends frontier machine learning with practical implementation—solving high-impact real-world problems in areas ranging from remote sensing and geology to environmental sustainability and asset underwriting.
Build and deploy machine learning models that power decision-making in asset evaluation, risk assessment, and project optimization
Design and own data pipelines that ingest, process, and analyze multi-modal datasets including satellite imagery, geospatial data, sensor streams, and third-party reports
Collaborate closely with geologists and investment analysts to translate raw scientific and commercial inputs into structured, predictive outputs
Develop experimental models for reserve estimation, cost forecasting, environmental impact, and other economic or scientific factors
Evaluate the feasibility of custom LLMs, agentic workflows, or reinforcement learning in support of key business goals
Lay the foundations for scalable ML infrastructure and reproducible research frameworks
PhD or MS in Machine Learning, Computer Science, Applied Math, Physics, or related field
3–7 years of experience in applied ML, ideally with exposure to complex unstructured data problems with geospatial analysis, simulation, remote sensing, healthcare data or industrial AI being a plus.
Experience building production-grade models using tools like Python, PyTorch, TensorFlow, XGBoost, or similar
Strong grasp of statistics, probabilistic modeling, and experimental design
Comfort working in ambiguous, fast-moving environments with limited supervision
Interest in natural resources, climate resilience, or emerging markets is a strong plus
Be a Co-Founder: This is a rare opportunity to join at inception and build technology that shapes investment outcomes in real-world economies.
Incredible Team: You'll work alongside leaders who’ve run high-impact global orgs (World Economic Forum, Saudi PIF) and made billion-dollar discoveries in geology.
True Impact: Your models won't just live in notebooks—they’ll directly influence how capital is allocated to critical infrastructure and emerging economies.
Adventurous Mission: Work between NYC, London, and field sites across LATAM, Africa, and Central Asia, tackling problems no one else is solving.