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Marketing Data Scientist

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United Kingdom Cranleigh STEM, Sustainability & SHEQ Recruitment Full time

Specialising in cutting-edge RNA technologies, this dynamic company aims to revolutionise the industry with groundbreaking advancements. As they expand their team, they are searching for a talented Data Scientist who possesses a strong background in applying machine learning techniques to real-world datasets.
To succeed in this role, you must have a solid background in machine learning, strong programming skills, and a passion for innovation in the field of bioinformatics/apply this to a novel type of RNA sequencing data. Data Scientist responsibilities
Data Exploration & Feature Engineering: Work with raw data, apply preprocessing techniques, and extract meaningful features to feed into machine learning models.
• Algorithm Selection & Implementation: Research and implement suitable machine learning algorithms (e.g., decision trees, neural networks, clustering) based on problem requirements.
• Evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score) and improve model performance through techniques like hyperparameter tuning, regularisation, and cross-validation.
• Production Deployment: Deploy machine learning models into production environments, monitor their performance, and ensure scalability and reliability.
• Collaboration: Work with researchers, and bioinformaticians to understand project requirements and deliver insights that support research and clinical decisions.
• Data Scientist requirements
• D. in Data Science, Computer Science, Statistics, Bioinformatics, or a related field.
• Demonstrated experience in applying machine learning techniques to real-world datasets, preferably in healthcare or cancer research.
• Proficiency in Python and related libraries (e.g., Hands-on experience working with high-dimensional, sparse data commonly encountered in fields such as natural language processing (e.g., text data), recommendation systems, or sensor networks.
• Proficiency in handling missing, incomplete, or sparse data using techniques such as imputation, matrix factorization, or dimensionality reduction (e.g., Strong knowledge of machine learning algorithms, especially ensemble methods like Random Forests.
• Familiarity with data preprocessing, feature engineering, and model evaluation techniques.

£Comp + company benefits + opportunity to join a fast-growing start-up working on groundbreaking technology.

Data Scientist/Machine Learning/Data Sets/Python/Algorithms