Senior AI Research Scientist

As a Senior AI Research Scientist at iGenius, key responsibilities include leading research initiatives aimed at developing innovative AI models for financial applications, designing algorithms for portfolio optimization and risk analytics, and collaborating with global teams to implement these models in real-world production systems. You will also have the chance to publish your findings in peer-reviewed journals and share insights at industry conferences, ensuring that your work contributes to thought leadership within the AI space.

To qualify for the Senior AI Research Scientist role at iGenius, candidates must possess a Ph.D. in Computer Science, Machine Learning, or a related field, alongside a minimum of 8 years of experience in AI research, particularly in financial services. Strong programming skills in Python, expertise in applied machine learning fields, and a proven track record in deploying large language models are crucial for success in this role.

At iGenius, we are committed to supporting the professional growth of our team members. Senior AI Research Scientists can expect opportunities for collaboration on international projects, the chance to publish research in prestigious journals, and the ability to present findings at top industry conferences. Furthermore, our company culture encourages continuous learning and innovation, providing a stimulating environment to advance your skills.

iGenius offers a competitive compensation structure for the Senior AI Research Scientist position, which includes a base salary, performance-based bonuses, and additional components tailored to individual experience. Moreover, all roles at iGenius include comprehensive benefits as part of the total compensation package, ensuring that our team members are rewarded for their contributions.

In the role of Senior AI Research Scientist at iGenius, you will utilize various technologies and tools including advanced Python programming, data science libraries such as NumPy, Pandas, and Scikit-learn, and work with large language models and MLOps pipelines. You will also engage with Transformer/Attention architectures to enhance tasks such as text summarization and entity extraction, ensuring that your work is aligned with the latest industry advancements.

When answering this question, be specific about the types of algorithms you've developed and how they were applied in financial scenarios, such as portfolio optimization or trading strategies. Highlight any challenges you faced and how you overcame them, emphasizing your problem-solving skills and the impact of your work on business outcomes.

Here, you should provide details on your experience with large language models, including specific projects where you’ve implemented them. Discuss your familiarity with prompt engineering, fine-tuning processes, and any MLOps pipelines you have developed to enhance LLM applications, illustrating your depth of knowledge in this area.

When discussing collaboration, emphasize your communication strategies and your willingness to share knowledge with team members across different regions. Provide examples of successful collaborations, outlining your role and how teamwork led to effective solutions in AI projects.

Discuss your publication history, highlighting significant papers and the impact they have had within your field. Explain how publishing contributes to thought leadership and your commitment to advancing AI research. If applicable, mention any collaborations with industry or academia that led to these publications.

Explain your methodology for developing robust AI models, which may include rigorous testing and validation practices. Additionally, discuss the importance of explainability in AI, especially in financial applications, and how you integrate techniques to make models interpretable for stakeholders.

Provide insights into the specific methods and tools you utilize for monitoring AI applications. Discuss how you handle model drift and performance issues, outlining your experience with end-to-end MLOps/LLMOps pipelines to ensure ongoing model effectiveness.

Discuss your strategies for staying informed about the latest trends and advancements in AI, such as attending conferences, participating in workshops, and reading academic journals. Emphasize your dedication to continuous learning and how it benefits your work in the field.

In your response, describe a specific challenging project related to AI in finance. Highlight the obstacles you faced, the strategies you employed to overcome them, and the resulting impact of your work, demonstrating both technical and soft skills.

When addressing this question, talk about your hands-on experience with Transformer architecture, outlining any specific projects that utilized it for tasks such as text summarization or entity extraction. Discuss the advantages of this architecture and how it has improved your work outcomes.

Share an example where you took on a leadership role in an AI project, detailing your responsibilities and the outcomes of your leadership. Highlight the collaborative nature of the project and how you motivated your team to achieve its objectives.