Data Scientist

Role summary

Measure how well AI models and features perform, build predictive models where classical machine learning fits better than an LLM, and turn usage data into decisions.

Responsibilities

Design evaluation methods and build test datadacodataasets: expected answers, edge cases, multilingual and adversarial prompts

Run evaluations and score the results with automated metrics and LLM-as-judge, calibrated against human review

Benchmark models on quality, latency and cost, and recommend which to use

Analyse errors, including false positives and false negatives of safety and privacy filters

Build and validate predictive and classification models on tabular, time-series and signal data

Add evaluation tests to CI pipelines, so a regression blocks the releacompse

Analyse usage, cost and quality data, and build reports and dashboards

Technical requirements

Degree in data science, statistics, computer science or a related field

Python with pandas, NumPy and scikit-learn

Statistics: precision, recall, confidence intervals, hypothesis testing

SQL

Git

Nice to have

More experience is a plus: professional years in data science or applied machine learning, and models used in production

Experience evaluating NLP or LLM systems

PyTorch or TensorFlow

Time-series or signal-processing models

LLM evaluation tools: Langfuse, Ragas, promptfoo

Experiment tracking with MLflow or Weights & Biases

Experience mentoring other data scientists

A second language, or a background in healthcare or another regulated domain

Desired Candidate Profile

Degree in data science, statistics, computer science or a related field

Python with pandas, NumPy and scikit-learn

Statistics: precision, recall, confidence intervals, hypothesis testing

SQL

Git