Python Developer (Mathematical Financial Risk & ML Models) - New York, NY (Hybrid Onsite) - CG

Role: Python Developer (Mathematical Financial Risk & ML Models)

Locations: New York, NY (Hybrid Onsite)

Duration: 12+ Months Contract

F2F Interview Highly Preferred - For Local Candidates.

Note: Candidate needs to be in the office 3-4 Days every week. Local or candidates from adjacent states only.

Job Description:

• As a successful candidate you will be given an opportunity to acquire and develop knowledge from related fields
• Collaborate with stakeholders throughout the organization to develop project plans of delivering objects and timelines of model development and implementation.
• Develop risk models in Python/R used by risk teams for regulatory stress testing submission and company risk management 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 Also apply various statistical and analytical tests for validating models and results
• Develop presentation decks using visual analytics tools and techniques JupyterHub Python.
• Apply data mining data modelling and machine learning techniques to analyze large financial datasets and enhance the model performance.

Qualifications

• Undergraduate Degree in a quantitative field, computer science, financial engineering, mathematics, data science or engineering.
• Experience using one or more programming languages Python.
• 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.