Job Description
Are you passionate about using data to drive innovation in drug discovery? We are an international data science service company dedicated to providing exceptional data curation, governance, bioinformatics analysis, and more to clients in pharmaceuticals, biotech, foundations, government, and healthcare. We are excited to invite a talented Biomedical Knowledge Graph Data Scientist to join our dynamic and supportive team.
About Us:
We are committed to creating a fast-paced and engaging work environment where our employees can thrive and grow both professionally and personally. We believe that our team members are our greatest asset, and we offer opportunities for continuous learning and development.
Key Responsibilities:
Design and Develop: Create scalable data pipelines to implement and execute knowledge graph embeddings (KGE) within drug discovery knowledge graphs.
Assess and Optimize: Conduct comprehensive evaluations of KGE models to determine their effectiveness in biomedical applications and enhance their predictive performance.
Experiment and Explore: Develop and execute tests to understand how different training methodologies and settings influence model performance.
Enhance Precision: Utilize advanced techniques to optimize hyperparameters, improving model accuracy and adaptability.
Collaborate Effectively: Work alongside interdisciplinary teams to ensure that KGEs are relevant to real-world drug discovery challenges and adhere to standards for equitable evaluation and replicability.
Share Insights: Compile and present findings and recommendations to improve KGE model assessment practices, contributing to the broader knowledge of biomedical AI.
Qualifications:
A Master's or Ph.D. in Data Science, Bioinformatics, Computer Science, or a related field.
Proven experience in developing and managing data pipelines and large datasets.
Proficiency in Python programming, especially with PyTorch and libraries such as PyG and PyKEEN.
Practical knowledge of knowledge graphs, machine learning techniques, and graph embedding models, with a focus on real-world applications.
Familiarity with biomedical knowledge graph platforms, including Disqover, PrimeKG, Hetionet, or BioKG.
Experience in fine-tuning parameters and optimizing models, particularly with Bayesian optimization methods.
Strong analytical skills with the ability to communicate complex ideas to both technical and non-technical audiences.
Preferred Qualifications:
Experience working with biomedical datasets or in the drug discovery domain.
Understanding of computational biology and systems pharmacology principles.
Familiarity with evaluation metrics and best practices for ensuring model reproducibility in scientific research.
Soft Skills:
Excellent relationship management skills with scientific stakeholders.
Ability to translate project requirements into effective business and technical solutions.
Self-motivated, dedicated, and hardworking, with a commitment to delivering quality work.
Strong communication skills, enabling effective interactions at all organizational levels.
Detail-oriented and organized, with a collaborative approach to remote work.
What We Offer:
We provide a competitive salary and benefits package, fostering a culture of growth and collaboration. Join us in making a meaningful impact in the world of biomedical research and drug discovery!
Employment Type: Full-Time
Salary: $ 115,000.00 157,000.00 Per Year
Job Tags
Full time,