BioTalent
A leading mid-sized biotech is seeking a highly skilled Machine Learning professional with experience in data mining to analyze large datasets from public domain sources and internal data. The ideal candidate will have a strong background in machine learning, data analysis, and programming. The primary objective of this role is to leverage AI, computational biology, and bioinformatics methods to classify and process R&D data, identifying potential opportunities for drug optimization and protein targets for immunotherapy.
Key Responsibilities:
Data Mining:
Apply data mining techniques to large datasets from clinical trials, using desensitized data to identify patterns, trends, and correlations. Machine Learning Model Development:
Design, develop, and train machine learning models to classify and process R&D data, identifying potential opportunities for drug optimization. Protein Target Identification:
Use AI, computational biology, and bioinformatics methods to identify protein targets for immunotherapy in cancers and other diseases. Data Analysis:
Analyze and interpret results from data mining and machine learning models, providing insights and recommendations to cross-functional teams. Collaboration:
Work closely with researchers, clinicians, and other stakeholders to ensure that data analysis and machine learning models are aligned with research goals. Communication:
Present findings and results to both technical and non-technical audiences through reports, presentations, and visualizations. Requirements:
Education:
Masters or Ph.D. in Computer Science, Machine Learning, Biostatistics, Bioinformatics, or a related field. Experience:
3+ years of experience in machine learning, data mining, or a related field, with a focus on clinical trial data analysis. Proficiency in programming languages such as Python, R, or SQL. Experience with machine learning frameworks such as TensorFlow or PyTorch. Familiarity with data visualization tools such as Tableau, Power BI, or D3.js. Domain Knowledge:
Strong understanding of clinical trial design, biostatistics, and immunotherapy principles. Communication Skills:
Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams Familiarity with containerization using Docker. Knowledge of data security and compliance regulations such as HIPAA. Experience with agile development methodologies and version control systems such as Git
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Data Mining:
Apply data mining techniques to large datasets from clinical trials, using desensitized data to identify patterns, trends, and correlations. Machine Learning Model Development:
Design, develop, and train machine learning models to classify and process R&D data, identifying potential opportunities for drug optimization. Protein Target Identification:
Use AI, computational biology, and bioinformatics methods to identify protein targets for immunotherapy in cancers and other diseases. Data Analysis:
Analyze and interpret results from data mining and machine learning models, providing insights and recommendations to cross-functional teams. Collaboration:
Work closely with researchers, clinicians, and other stakeholders to ensure that data analysis and machine learning models are aligned with research goals. Communication:
Present findings and results to both technical and non-technical audiences through reports, presentations, and visualizations. Requirements:
Education:
Masters or Ph.D. in Computer Science, Machine Learning, Biostatistics, Bioinformatics, or a related field. Experience:
3+ years of experience in machine learning, data mining, or a related field, with a focus on clinical trial data analysis. Proficiency in programming languages such as Python, R, or SQL. Experience with machine learning frameworks such as TensorFlow or PyTorch. Familiarity with data visualization tools such as Tableau, Power BI, or D3.js. Domain Knowledge:
Strong understanding of clinical trial design, biostatistics, and immunotherapy principles. Communication Skills:
Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams Familiarity with containerization using Docker. Knowledge of data security and compliance regulations such as HIPAA. Experience with agile development methodologies and version control systems such as Git
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