Machine Learning (ML) Engineer
Description of the role & key responsibilities:
As an ML engineer you will harness the power of data analytics to redefine financial services and deliver cutting-edge data-driven solutions that transform customer experiences and streamline operations.
- Engage in exploratory data analysis and build robust models by analyzing large and diverse datasets, including customer behavior, transaction records, and risk assessment data, to extract actionable insights.
- Develop, optimize, and maintain end-to-end machine learning pipelines, encompassing data preprocessing, feature extraction, model training, and evaluation.
- Deploy models into production environments, ensuring seamless integration with existing systems.
- Monitor model performance and retrain models as needed to maintain accuracy and reliability over time.
- Work closely with product managers, data scientists, software engineers, and business stakeholders to design, develop, and implement ML models that address key challenges in the fintech and insurance space.
- Stay up-to-date with the latest advancements in machine learning, artificial intelligence, and fintech-insurance applications. Leverage new techniques, frameworks, and tools to enhance model performance and address emerging business needs.
- Contribute to the development and documentation of best practices, standards, and guidelines for ML engineering within the organization.
Required qualifications and skills:
- 2-4 years of hands-on experience in designing, building, and deploying ML models in production environments.
- Proven track record of implementing live projects.
- Strong understanding of both supervised and unsupervised learning techniques, including but not limited to regression, classification, clustering, dimensionality reduction, and anomaly detection.
- Proficiency in Python and relevant ML libraries (e.g. PyTorch, Scikit-Learn, Pandas, DeepML).
- Ability to create insightful visualizations using tools like Matplotlib, Seaborn, or Plotly.
- Experience with big data processing frameworks such as Spark, Hadoop, or similar.
- Familiarity with cloud platforms like AWS, GCP, or Azure for deploying ML models.
- Experience with databases (SQL and NoSQL) and data storage solutions such as PostgreSQL, MongoDB, or AWS RDS.
- Experience with ETL processes, data preprocessing, and feature engineering.
- Strong analytical and problem-solving skills to be able to interpret complex data and present insights clearly to technical and non-technical stakeholders.
- Experience in developing and optimizing algorithms for real-world applications, especially in the context of financial services, risk analysis, fraud detection, and customer personalization.
- Experience in deploying and monitoring ML models in live production environments using tools like Docker, Kubernetes, or cloud-based services (e.g. AWS SageMaker, Google AI Platform).
- Experience with A/B testing and model retraining pipelines is a plus.
- Excellent communication skills, with the ability to work collaboratively in a fast-paced, team-oriented environment.
Are you ready to analyze and interpret complex data, design, maintain and monitor ML pipelines? Join our dynamic team and drive innovation in a rapidly evolving sector.