Research Data Scientist II/Sr Research Data Scientist

As a Research Data Scientist II/Sr Research Data Scientist at Iambic Therapeutics, you'll be responsible for executing the complete machine learning project lifecycle, developing and deploying models like NeuralPLexer and Enchant, and collaborating with internal teams for effective communication of results. Additionally, you'll perform research in machine learning literature, refactor and package code, and train new employees in machine learning practices.

To qualify for the Research Data Scientist II/Sr Research Data Scientist role at Iambic Therapeutics, you should hold a PhD or an equivalent degree in machine learning or a computational STEM field. A strong background in deep learning as it pertains to biology, medicine, or chemistry is also essential, along with experience in applying machine learning techniques to structural biology or drug design problems.

Iambic Therapeutics fosters an environment of continuous learning by allowing Research Data Scientists to conduct individual and group research in the machine learning field. In addition, you'll also have the chance to train new employees, thereby reinforcing your own knowledge while sharing insights on machine learning methodologies and practices.

Research Data Scientists at Iambic Therapeutics can expect to be deeply involved in significant projects that shape the future of drug discovery. You will have the chance to work on pioneering models, engage in collaborative brainstorming sessions with multidisciplinary teams, and even present your findings at external conferences, furthering your career and contributing to groundbreaking advancements.

Iambic Therapeutics is unique in its commitment to diversity and inclusion, creating an enriching environment where various backgrounds and experiences fuel innovation. Along with competitive pay and generous benefits, our state-of-the-art facility in San Diego provides amenities designed for enhancing work-life balance, ensuring our Research Data Scientists feel valued and motivated.

Highlight your relevant hands-on experience with neural and transformer models by discussing specific projects where you've successfully implemented, trained, and evaluated these models. Include details about the specific tasks you undertook, the data sets you worked with, and the outcomes you achieved.

When asked about the differences, provide a concise explanation of both models. Emphasize that NeuralPLexer focuses on protein-ligand structure prediction using equivariant diffusion techniques, while Enchant is a multitask transformer model trained on a vast range of biomedical data. This shows your understanding of how each model operates and its intended application.

Share your approach to training large-scale models, detailing the methodologies you use for data preparation, model selection, hyperparameter tuning and evaluation metrics. Discuss challenges you've overcome and any specific tools or frameworks you've applied to make the process smoother.

Choose a relevant project and describe the problem, your role in it, the techniques you utilized, and the impact of the solution. Be specific about how deep learning was pivotal in achieving favorable results, illustrating your capability to apply technical skills to real-world scientific challenges.

Discuss strategies for breaking down complex machine learning outcomes into digestible insights. For example, you might use visualizations, relatable analogies, or executive summaries to help non-technical team members understand your findings and how they apply to their work.

Demonstrate your commitment to professional growth by discussing specific journals, blogs, or conferences you follow. Mention any relevant courses you've completed or groups you participate in, as this reflects a proactive approach to staying updated in a fast-paced field.

Highlight specific challenges related to data quality, model performance, or collaboration with cross-functional teams. Discuss the steps you took to analyze and address these issues, demonstrating your problem-solving and critical thinking skills.

Address this scenario by outlining your approach to facilitating constructive discussions. Emphasize listening to all viewpoints, summarizing key arguments, and guiding the team towards a consensus that aligns with overall project goals, showcasing your teamwork and leadership skills.

Explain your approach toward establishing coding standards, implementing regular code reviews, and utilizing version control systems. Highlight any tools or practices you use to facilitate testing and documentation, illustrating your commitment to maintaining high code quality.

Discuss the importance of ethical considerations in machine learning, particularly concerning data privacy and algorithmic bias. Emphasize the responsibility of scientists to ensure their work promotes fairness and transparency while addressing potential societal impacts.