About the job
About CodeNinja
CodeNinja is a pioneering AI solutions provider dedicated to assisting enterprises, governmental bodies, and software buyers in the creation and management of intelligence-driven systems tailored for mission-critical operations. Our expertise lies in the seamless integration of AI into real-world applications, where we couple robust engineering principles with AI-native methodologies to deliver measurable benefits, resilience, and sustainable ownership for our clients. Our extensive global presence, supported by AI Labs, AI Pods, and Global Capability Centers, empowers our teams to collaboratively design scalable platforms across diverse regions and time zones.
Role Overview
We are looking for a Senior Machine Learning Engineer (Lead) with 5-6 years of experience to architect and oversee the development of sophisticated time-series forecasting solutions aimed at high-impact financial applications. This position demands profound technical proficiency in machine learning, extensive knowledge of the financial domain, and the capability to transform research-driven models into production-grade systems within critical operational environments.
In this role, you will be instrumental in constructing robust, interpretable, and scalable forecasting models that bolster financial risk management, foreign exchange exposure assessments, treasury planning, and multi-entity forecasting.
Key Responsibilities
1. Technical Leadership & Model Development
- Lead the comprehensive development of time-series forecasting solutions , from data acquisition and feature engineering to deployment and ongoing monitoring.
- Design scalable machine learning pipelines specifically for financial forecasting scenarios.
- Develop and evaluate classical statistical models (ARIMA, SARIMA, ETS) alongside machine learning techniques (XGBoost, Gradient Boosting, Prophet, etc.).
- Create advanced time-series features, including lag structures, rolling statistics, seasonal/trend decomposition, Fourier terms, regime indicators, and integration of exogenous variables.
- Implement time-aware cross-validation methodologies and Bayesian hyperparameter optimization.
- Conduct thorough backtesting in accordance with financial validation standards.
2. Financial Modeling & Risk Analytics
- Develop forecasting models for:
- Foreign exchange exposure forecasting
- Treasury cash flow predictions
- Intercompany netting optimization
- Financial risk assessments
- Multi-currency and multi-entity forecasting
- Assess model uncertainty and incorporate confidence intervals to enhance financial decision-making.
- Execute anomaly detection and outlier analysis in financial time-series datasets.
- Ensure models are aligned with financial reporting cycles, treasury constraints, and accounting standards.
3. Production Deployment & MLOps
- Design reproducible machine learning workflows and production-ready pipelines.
- Implement model versioning, monitoring, drift detection, and automated retraining strategies.
