Design, develop and refine machine learning models ranging from linear and logistic regression through to tree-based methods, ensemble approaches and advanced algorithms
Develop proof of concepts and translate exploratory insights into structured modelling approaches
Design and execute statistical offline simulations to validate modelling approaches prior to live experimentation
Plan, implement and analyse controlled A/B tests, ensuring statistical robustness and clear interpretation of results
Define success metrics aligned to business objectives and ensure appropriate evaluation frameworks are in place
Analyse model performance using appropriate metrics such as precision, recall, ROC-AUC, log loss, calibration measures and model-specific metrics
Identify potential sources of bias, data leakage and experimental confounding, and proactively mitigate associated risks
Work closely with Product, Engineering and other stakeholders to prioritise opportunities and ensure models deliver measurable value
Communicate complex modelling concepts, trade-offs and experimental results in a clear and accessible way to both technical and non-technical audiences
Contribute to raising the overall standard of modelling practices within the team

3 to 4 years of experience building, evaluating and iterating on machine learning models using large and complex data sets
Strong academic background in Statistics, Mathematics, Computer Science or a related quantitative discipline
Deep understanding of statistics, machine learning and experimental design principles
Demonstrated experience running A/B tests, designing offline validation strategies and interpreting experimental outcomes
Strong grasp of model evaluation metrics and the ability to select appropriate performance measures based on business context
Strong programming skills in Python with experience using libraries such as NumPy, Pandas and related tools for data manipulation and analysis
Experience working with cloud-based data infrastructure such as BigQuery and AWS services including S3 and SageMaker
Experience using Jupyter notebooks for exploratory analysis, modelling and communicating findings
Ability to move comfortably between high level problem framing and detailed quantitative analysis
Strong product orientation with experience collaborating closely with cross-functional teams
Excellent written and verbal communication skills, with the ability to explain technical concepts clearly and confidently.

25 days of paid holiday, with extra days added at 3 and 5 years of service.
Fully remote working, plus up to 20 days each year to work from anywhere in the world.
Cobee Wellbeing platform with flexible benefits: meal vouchers, travel vouchers, gym memberships, private medical etc..
A €250 allowance towards essential home office technology to help you stay connected and productive.

Senior Data Scientist

Key skills