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Data Scientist
2 months ago
Cranleigh Scientific is a pioneering biotech startup on the cusp of exponential growth, specializing in cutting-edge RNA technologies. We're seeking a talented Data Scientist to join our dynamic team and contribute to groundbreaking advancements in the industry.
Key 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 based on problem requirements.
- Model Evaluation & Optimisation: Evaluate model performance using appropriate metrics 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.
- Reporting: Present findings through detailed reports, visualisations, and presentations for both technical and non-technical audiences.
- Documentation: Maintain thorough documentation of methodologies, experiments, and results to ensure reproducibility and knowledge sharing.
- Master's or Ph.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., scikit-learn, pandas, numpy, matplotlib, seaborn).
- Experience with deep learning frameworks (e.g., Keras, PyTorch).
- 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., PCA, SVD).
- Strong knowledge of machine learning algorithms, especially ensemble methods like Random Forests.
- Ability to handle complex datasets and derive actionable insights.
- Familiarity with data preprocessing, feature engineering, and model evaluation techniques.
What We Offer: Competitive compensation, company benefits, and the opportunity to join a fast-growing start-up working on groundbreaking technology.
