| DATA SCIENTIST WITH PHARMA DOMAIN EXP (LOCAL TO MA OR RI OR NH) at Remote, Remote, USA |
| Email: [email protected] |
| From: Maseera Khan, Absolute IT [email protected] Reply to: [email protected] Job Description Title: Data Scientist (This position is hybrid for 2-3 days preferred, must be a local to MA/NH or RI) Candidate must have an active and an updated LinkedIn ID with a picture and current location updated (REQUIRED!!) Company: Takeda Location: Cambridge hybrid Team: Data Integration and Analytics Why Open: new initiatives Start: ASAP Duration: 6 moths to start (potential to extend) Interview Process: 2-3 rounds Notes: This position is EXEMPT from overtime Job Description: This role provides an exciting opportunity to be at the forefront of biomedical AI, contributing to projects that can significantly impact healthcare outcomes. We are seeking an innovative Data Scientist to join our team, where you will be instrumental in designing, building, and optimizing pipelines for the performance evaluation of knowledge graph embeddings (KGE) in the drug discovery domain. You will contribute to the application of KGEs in enhancing target identification, drug repurposing, and other critical areas within drug discovery. Your responsibilities will include implementing KGE models, assessing their predictive performance, and optimizing hyperparameters and configurations to ensure reliable and reproducible results that advance and improve patient health. Key Responsibilities: Design and build scalable data pipelines to implement and execute KGE models on drug discovery knowledge graphs. Conduct comprehensive evaluations of KGE models, including ULTRA, ComplEx, DistMult, RotatE, TransE, and TransH, to assess their performance specifically in biomedical applications. Analyze model performance across various factors, such as hyperparameters, model initialization, dataset splits, and different knowledge graphs, including Disqover, PrimeKG, Hetionet, and BioKG. Develop and execute experiments to understand the impact of diverse training setups and configurations on model effectiveness. Implement hyperparameter optimization techniques to boost model accuracy and generalizability, using tools such as Optuna and PyKEEN. Collaborate with cross-functional teams to ensure that KGEs align with real-world drug discovery applications and adhere to fair evaluation and reproducibility standards. Document and communicate findings and recommendations to improve KGE model evaluation practices and contribute to the broader knowledge base in biomedical AI. Qualifications Proven experience in building and managing data pipelines and handling large datasets. Strong programming skills in Python, with proficiency in PyTorch and libraries like PyG and PyKEEN. Experience with knowledge graphs, machine learning, and graph embedding models and their applications. Familiarity with biomedical knowledge graphs, such as Disqover, PrimeKG, Hetionet, or BioKG. Demonstrated expertise in hyperparameter tuning and optimization, including the use of Bayesian optimization techniques. Excellent analytical skills and the ability to communicate complex ideas effectively to both technical and non-technical stakeholders. Preferred Qualifications: Masters or Ph.D. in Data Science, Bioinformatics, Computer Science, or a related field. Experience with biomedical datasets or in the drug discovery field. Familiarity with computational biology and systems pharmacology principles. Strong understanding of evaluation metrics and best practices for ensuring model reproducibility in scientific research. Keywords: artificial intelligence information technology Idaho Massachusetts New Hampshire Rhode Island DATA SCIENTIST WITH PHARMA DOMAIN EXP (LOCAL TO MA OR RI OR NH) [email protected] |
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| Fri Nov 01 19:42:00 UTC 2024 |