Senior Vice President, Full Stack Engineer at BNY

Manchester, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

11 Feb, 26

Salary

0.0

Posted On

13 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Data Pipelines, Visual Analytics, Data Mining, Data Modelling, Machine Learning, Statistical Techniques, Financial Models, Risk Metrics, Sensitivity Analysis, Attribution Analysis, Communication Skills, Team Coordination, Quantitative Insight, Model Performance, Programming Languages, SQL

Industry

Financial Services

Description

Your mission: transform regulatory stress and credit loss modeling into a reliable, scalable analytics capability. You will implement ICAAP/ICARA, UK Stress Testing models, engineer robust Python data pipelines, build decision-ready visuals, and dissect capital & loss movements. Next phase broadens into TCAP —bringing richer products, more scenarios, and higher performance demands. If you enjoy pairing quantitative insight with disciplined engineering, we'd like to meet you. Design and build the execution workflow of models to forecast Balance Sheet, Fee Revenues, Macroeconomic Factors, Expense and calculate risk metrics under various stress scenarios, sensitivity & attribution analysis. Coordinate with different functional teams to implement models and coordinate coding, testing, implementation and documentation of financial models. Develop processes and tools to monitor and analyze model performance to ensure the expected application performance levels are achieved. Develop presentation decks using visual analytics tools and techniques. Apply data mining, data modelling and machine learning techniques to analyze large financial datasets and enhance the model performance. Master/MBA/PhD's Degree in a quantitative field (computer science, financial engineering, mathematics, data science or engineering) Experience using one or more programming languages (Python, R, C++, Java, Matlab, etc.) and manipulating data using SQL and Pandas Excellent written and verbal communication skills for coordination across teams Understanding of design, development and implementation of mathematical, financial risk and ML models Relevant work experience in a related field based on education level Knowledge of advanced statistical techniques and concepts (regression, time series analysis, statistical tests, etc.)

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Responsibilities

Transform regulatory stress and credit loss modeling into a reliable, scalable analytics capability. Design and build the execution workflow of models to forecast various financial metrics and coordinate with different functional teams for implementation.