DescriptionAre you passionate about data science with a comprehensive understanding of analytical principles, tools, technologies, and the capability to convey insights to both executive and non-technical audiences? If so, this could be the ideal opportunity for you.
As a Applied Artificial Intelligence- Machine Learning- Senior Associate, on the Instrumentation & Metrics (I&M) team, you are in integral part of the team that will be responsible for leveraging your expertise in data science; machine learning to develop and maintain production grade models using various analytical techniques. You will provide ad-hoc analytics support to the Payments organization, transforming complex data into actionable insights. Additionally, you will guide the team on best practices and techniques in data science; machine learning, ensuring the effective use of data to drive business decisions.
The Data Science & Analytic Solutions (DSAS) team is responsible for analytic support of the Commercial Investment Bank (CIB) Payments organization. As key partners with Payments, the DSAS team is central in adding data insights to help form business strategy through collaboration with business stakeholders. Key drivers to success are the effective management of data assets, analytical/visualization tools, disciplines and controls used for business readiness, process, and procedures. This includes creation of project plans, data workflows, dashboards, and ad-hoc analyses that contribute to strategic objectives.
Job responsibilities
- Designs, develops, and deploy machine learning models in production environments
- Utilizes Python and machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, … to develop models
- Identifies and selects appropriate features to improve model predictions
- Performs data preprocessing and feature engineering to prepare datasets for model training
- Address machine learning challenges with strong analytical and problem-solving skills
- Communicates complex machine learning concepts and results effectively to diverse audiences across various levels of the banking organization, including those unfamiliar with advanced machine learning techniques
- Participates in training sessions and workshops to enhance skills and knowledge
- Research and implementation of state-of-the-art techniques to improve model performance
Required qualifications, capabilities, and skills
- Bachelor’s or Master’s degree in machine learning, artificial intelligence, statistics, mathematics, data science, or a closely related technical field, with 4+ years of hands-on experience in machine learning
- Demonstrated expertise in machine learning algorithms, model development, and deployment. Proven track record of building and implementing machine learning models in production environments
- Proficiency in Python, with extensive experience using machine learning frameworks and libraries such as TensorFlow, PyTorch, and Scikit-learn. Familiarity with ML-specific tools and platforms like MLflow, or similar
- Ability to perform model optimization, hyperparameter tuning, and evaluation using techniques such as cross-validation, A/B testing, and performance metrics analysis
- Problem-Solving Skills: Strong analytical and problem-solving skills specifically related to machine learning challenges, including data preprocessing, feature engineering, and model selection
- Excellent written and verbal communication skills to effectively convey complex machine learning concepts and results to both technical and non-technical stakeholders
Preferred qualifications, capabilities, and skills
- Familiarity with the financial services industry and its specific machine learning applications
- Experience in natural language processing (NLP) and advanced analytics techniques relevant to machine learning
- Understanding of financial products and services, including trading, investment, and risk management, with a focus on machine learning applications