Junior Data Scientist- Engage grade 7

Model Development:

2.1.1 Design, develop and implement statistical and machine learning models to support pricing evaluations and predict future cashflows. Translate model outputs into pricing recommendations that reflect expected cashflows, uncertainty and business objectives.

2.2 Data Management and Analysis:

2.2.1 Prepare, validate and analyse pricing and evaluation data to identify trends, portfolio characteristics and cashflow drivers. Maintain evaluation data in accordance with agreed data management procedures.

2.3 Feature Engineering:

2.3.1 Identify, create and select relevant features from historical cashflows, account attributes and portfolio characteristics to improve pricing and cashflow predictions. Assess feature quality, stability and availability at the time of pricing.

2.4 Model Validation, Calibration and Monitoring:

2.4.1 Validate and back-test pricing and cashflow models to assess accuracy, reliability and suitability across portfolios. Calibrate model parameters and assumptions using observed performance, monitor predicted against actual cashflows and investigate deviations to recommend model improvements.

2.5 Automation:

2.5.1 Develop and maintain automated workflows for running pricing models, model calibrations and model monitoring. Incorporate data checks, repeatable calculations and exception reporting to improve consistency, reduce manual effort and support timely pricing delivery.

2.6 Collaboration:

2.6.1 Work closely with finance, operations and other teams to understand pricing requirements, agree model assumptions and translate business needs into analytical solutions. Provide advice and analytical support to pricing evaluations and related processes.

2.7 Reporting:

2.7.1 Present pricing analysis, cashflow forecasts, model performance and calibration results through reports, dashboards and presentations. Explain assumptions, limitations and material deviations clearly to support pricing discussions and decisions.

2.8 Research and Innovation:

2.8.1 Evaluate developments in statistics, machine learning, AI, cashflow forecasting and pricing analytics. Test and apply suitable methods to improve pricing model accuracy, calibration, monitoring and automation.

1.1                  Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.

1.2                  Experience in pricing or cashflow forecasting, predictive modelling, machine learning, and data analysis, preferably in the financial services industry.

1.3                  Proficiency in programming languages such as SQL, Python, R or SAS, and relevant statistical and machine learning libraries such as scikit-learn. Ability to prepare and analyse data and develop reproducible analytical workflows.

1.4                  Practical understanding of model validation, calibration and performance monitoring, with the ability to automate pricing model runs, calibration processes and monitoring reports.

1             Skills and behaviours

1.1                  Strong analytical and problem-solving skills.

1.2                  Creative and commercially informed thinking.

1.3                  Attention to detail and commitment to data and model quality.

1.4                  Self-motivated with the ability to manage competing pricing deadlines.

1.5                  Clear verbal and written communication of technical findings to business stakeholders.

1.6                  Ability to work independently and collaboratively across teams.