This is a full-time, on-site research and engineering role focused on estimating the risk-adjusted realised value of a B2B export deal. Not just whether a buyer is likely to want a product, but whether the resulting deal will actually be profitable once freight, duties, financing, disputes, returns, late payment, and other real-world losses are accounted for. These loss events are rare, highly variable in size, unfold over months, and are recorded only informally across correspondence and operational records, which makes estimating them a genuine technical problem rather than a reporting exercise.
You will design, build, and ship the models that turn this sparse, messy signal into reliable, decision-relevant value estimates, and validate whether ranking recommendations by risk-adjusted value improves real commercial outcomes over ranking by buyer relevance alone. This is a hands-on role: you will write the data-processing code, implement the models, build the evaluation harness, and be responsible for clean, reproducible, well-tested code end to end.
The work spans the full lifecycle: constructing supervised signal from informal records, building probabilistic loss models under sparsity, correcting for the statistical biases in historical sales data, and validating the end-to-end system through controlled evaluation. The first six months focus on label construction and the loss model. Factor decomposition, the re-ranking module, and end-to-end evaluation follow from there.
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