Overview: We are seeking a highly skilled Data Scientist to join our Applied AI Team. The ideal candidate will have a strong background in statistics, machine learning, and data analysis, with experience working in cross-functional teams. As a Data Scientist at AI Labs, you will be tasked with developing and implementing advanced analytics and machine learning solutions across business units within our company, including Clinical Trials, Regulatory, Business Intelligence, and Drug Manufacturing. The ideal candidate will be responsible for extracting valuable insights from complex datasets, developing predictive models, and collaborating with engineers and product owners to integrate AI solutions into our products and processes. Responsibilities: Collaborate with cross-functional teams to understand business needs and develop data-driven solutions. Design and implement machine learning models and statistical analyses to solve complex business problems. Develop and maintain data pipelines for efficient data processing and model deployment. Conduct exploratory data analysis to uncover patterns and insights in large datasets. Create visualizations and reports to communicate findings to technical and non-technical stakeholders. Qualifications: Proficient in Spanish and English in written and verbal communication. Strong understanding of statistics, machine learning, and data mining techniques. Expertise in Python programming, including proficiency with data science libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow). Strong understanding of data preprocessing, feature engineering, and model evaluation techniques. Experience with version control systems (e.g., Git) and collaborative development practices. Proven ability to translate complex technical concepts into clear, actionable insights for non-technical audiences. Familiarity with software engineering best practices and ability to write clean, maintainable code. Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and their machine learning services. Experience with big data technologies (e.g., Spark, Hadoop) and SQL databases is a plus. Knowledge of deep learning architectures and natural language processing is beneficial. Knowledge and experience in causal inference methodologies will be highly valued. Familiarity with Large Language Models (LLMs) and their applications in business contexts is a plus.
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