We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Data Scientist Lead within Asset & Wealth Management, you will utilize your quantitative, data science, and analytical skills to tackle complex problems. Your role will involve collaborating with various teams to design, develop, evaluate, and execute data science and analytical solutions, all while maintaining a deep functional understanding of the business problem at hand.
Job responsibilities
- Design, deploy and manage prompt-based models on LLMs for various NLP tasks in the financial services domain
- Conduct research on prompt engineering techniques to improve the performance of prompt-based models within the financial services field, exploring and utilizing LLM orchestration and agentic AI libraries.
- Collaborate with cross-functional teams to identify requirements and develop solutions to meet business needs within the organization
- Communicate effectively with both technical and non-technical stakeholders
- Build and maintain data pipelines and data processing workflows for prompt engineering on LLMs utilizing cloud services for scalability and efficiency.
- Develop and maintain tools and framework for prompt-based model training, evaluation and optimization
- Analyze and interpret data to evaluate model performance to identify areas of improvement
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Experience with prompt design and implementation or chatbot application
- Strong programming skills in Python with experience in PyTorch or TensorFlow
- Experience building data pipelines for both structured and unstructured data processing.
- Experience in developing APIs and integrating NLP or LLM models into software applications
- Hands-on experience with cloud platforms (AWS or Azure) for AI/ML deployment and data processing.
- Excellent problem-solving and the ability to communicate ideas and results to stakeholders and leadership in a clear and concise manner
- Basic knowledge of deployment processes, including experience with GIT and version control systems
- Familiarity with LLM orchestration and agentic AI libraries
- Hands on experience with MLOps tools and practices, ensuring seamless integration of machine learning models into production environment
Preferred qualifications, capabilities, and skills
- Familiarity with model fine-tuning techniques such as DPO and RLHF.
- Knowledge of Java, Spark
- Knowledge of financial products and services including trading, investment and risk management