Job Description

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 and applied this to a novel type of RNA sequencing data. As a key member of their startup team you will have the opportunity to shape the development of both biochemical and bioinformatic technologies contributing to groundbreaking solutions that make a realworld impact.

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.
• Model Evaluation Optimisation: Evaluate model performance using appropriate metrics (e.g. accuracy precision recall F1score) and improve model performance through techniques like hyperparameter tuning regularisation and crossvalidation.
• 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 nontechnical audiences.
• Documentation: Maintain thorough documentation of methodologies experiments and results to ensure reproducibility and knowledge sharing.

Data Scientist requirements

• Degree level qualification Masters or Ph.D. in Data Science Computer Science Statistics Bioinformatics or a related field.
• Demonstrated experience in applying machine learning techniques to realworld datasets preferably in healthcare or cancer research.
• Proficiency in Python and related libraries (e.g. scikitlearn pandas numpy matplotlib seaborn).
• Experience with deep learning frameworks (e.g. Keras PyTorch).
• Handson experience working with highdimensional 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.

Lifelancer ( ) is a talenthiring platform in Life Sciences Pharma and IT. The platform connects talent with opportunities in pharma biotech health sciences healthtech data science and IT domains.

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