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Cranleigh STEM has partnered with an innovative biotech startup poised for exponential growth. 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 Machine Learning Engineer 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 and applied this to a novel type of RNA sequencing data. As a key member of their start-up team, you will have the opportunity to shape the development of both biochemical and bioinformatic technologies, contributing to ground-breaking solutions that make a real-world impact.

Machine Learning Engineer 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, 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.

• 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.

Machine Learning Engineer requirements

• Degree level qualification - 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.

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

Machine Learning Engineer/Machine Learning/Data Sets/Python/Algorithms