Predictive modelling (machine learning)
hands on experience building, validating and interpreting predictive models in Python.
Time-series analysis and forecasting
experience with models that analyse data over time to make a future prediction.
Applied statistical analysis
ability to test for the statistical significance of a trend.
Creative feature engineering
must be able to use thousands of individual data points into a handful of organisational level predictors.

Advanced SQL & Data Architecture: Mastery of SQL with the ability to adapt queries across dialects (e.g., T-SQL to Snowflake).
Experience navigating complex relational databases to extract multi-modal datasets.
End-to-End Python Modelling: Hands-on experience using the Python data stack (Pandas, NumPy, Scikit-Learn) to build, validate, and deploy predictive models.
Forecasting & Time-Series Analysis: Expertise in analysing longitudinal data (data over time) using libraries like Prophet or Statsmodels to create forward-looking risk forecasts.
Creative Feature Engineering: A proven ability to transform messy, individual-level behavioural and clinical data into meaningful, aggregated organizational-level metrics.
Applied Statistics: Strong foundation in statistical significance testing, ensuring that discovered trends are mathematically sound and not just random noise.
Collaborative "Translator" Skills: Ability to bridge the gap between deep domain intuition and technical execution.
Must be able to explain complex model outputs to senior leadership and collaborate with Data Engineering to ensure model stability during warehouse migrations.
Data Ethics & Privacy: Experience working with sensitive clinical or wellbeing data, with a strict adherence to privacy standards (GDPR/POPIA/HIPAA) and anonymisation protocols.

Data Scientist

Key skills