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Data Scientist

2 months ago

Edinburgh, Edinburgh, United Kingdom Lifelancer Ltd Full time

About the Role

Lifelancer Ltd is seeking a highly skilled Data Scientist to join our team and contribute to the development of innovative solutions in the field of bioinformatics. As a Data Scientist, you will play a crucial role in analyzing and interpreting complex data sets, applying machine learning techniques to drive business decisions, and collaborating with cross-functional teams to deliver high-impact projects.

Key Responsibilities

  • Data Exploration and Feature Engineering
    • Work with large datasets, applying data preprocessing techniques to extract meaningful features and feed them into machine learning models.
    • Develop and implement data visualization tools to communicate insights to stakeholders.
  • Algorithm Selection and Implementation
    • Research and implement suitable machine learning algorithms, such as decision trees, neural networks, and clustering, based on problem requirements.
    • Collaborate with researchers and bioinformaticians to understand project requirements and deliver insights that support research and clinical decisions.
  • Production Deployment and Monitoring
    • Deploy machine learning models into production environments, ensuring scalability and reliability.
    • Monitor model performance and make data-driven decisions to optimize results.
  • Collaboration and Communication
    • Work closely with cross-functional teams, including researchers, bioinformaticians, and software developers, to deliver high-impact projects.
    • Communicate complex technical concepts to non-technical stakeholders, ensuring seamless collaboration and knowledge sharing.

Requirements

  • Education
    • Masters or Ph.D. in Data Science, Computer Science, Statistics, Bioinformatics, or a related field.
  • Experience
    • Demonstrated experience in applying machine learning techniques to real-world datasets, preferably in healthcare or cancer research.
    • Hands-on experience working with high-dimensional, sparse data commonly encountered in fields such as natural language processing, recommendation systems, or sensor networks.
  • Skills
    • Proficiency in Python and related libraries, such as NumPy, pandas, and scikit-learn.
    • Strong knowledge of machine learning algorithms, especially ensemble methods like Random Forests.
    • Familiarity with data preprocessing, feature engineering, and model evaluation techniques.