• Develop and deploy machine learning models: Build, train, and deploy machine learning models to solve complex business problems, ensuring they meet performance and scalability requirements.
• Data preprocessing and feature engineering: Transform raw data into formats suitable for machine learning algorithms, including data cleaning, feature selection, and feature engineering to improve model accuracy.
• Model evaluation and validation: Evaluate model performance using appropriate metrics, validate models to ensure they generalize well, and identify areas for improvement.
• Experiment design and A/B testing: Design and execute experiments to compare different models and approaches, including A/B testing to measure model impact in production.
• Years of Experience *
Minimum 6-7 years of relevant experience
Required Skills *
· Strong hands-on Python for machine learning and production-quality software development.
· Solid knowledge of supervised and unsupervised learning, feature engineering, model selection, cross-validation and performance metrics.
· Practical experience with common ML libraries such as scikit-learn, XGBoost, LightGBM, pandas and NumPy; deep learning frameworks where relevant.
· Good SQL skills and experience working with large, complex datasets.
· Experience productionizing models, including packaging, APIs, batch inference, containers and automated pipelines.
· Hands-on AWS knowledge, preferably including SageMaker, S3, ECR, ECS/EKS or Lambda, Step Functions and CloudWatch.
· Experience with Git-based development, code reviews, automated testing and CI/CD fundamentals.
· Understanding of model monitoring, data/model drift, explainability, reproducibility and model lifecycle governance.
· Ability to communicate technical findings clearly to both technical and business stakeholders.
Required Experience / Knowledge Areas *
· 6-7+ years across Data Science, Machine Learning Engineering or applied ML roles, with evidence of models deployed into production.
· Experience delivering end-to-end ML use cases rather than working only on infrastructure or platform administration.
· Experience in banking, financial services or another regulated industry is preferred.
· Understanding of model governance, validation and documentation expectations in controlled environments is advantageous.
Skills
Python
machine learning
AWS
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