Job Overview

Leverages creativity and machine learning expertise to help identify areas to apply advanced analytics in different parts of the organization.

Involved in defining the scope of data science products and the internal analytics roadmap. Develops clear, concise communication strategies for explaining relevant machine learning and data science concepts to business stakeholders.

Typical Activities

Contribute to stable, reliable product development work in cross-functional autonomous SCRUM teams

Build and train new production-grade algorithms that can learn from complex, high-dimensional data in order to uncover patterns from which machine learning models and applications can be developed

Write well-designed, testable, efficient code

Guide the transformation of machine learning research domain expertise in the areas of human data into viable prototypes

Research current and emerging industry tools, techniques and algorithms, and share these findings with colleagues or prepare and submit conference and journal articles

Essential Requirements

Demonstrable experience working on creating machine learning algorithms

Prior engineering project experience using relevant skills and technologies, especially with Python (Scikit-learn, Tensor Flow, Pandas, Numpy, Scipy)

Experience with building, testing, measuring, and deploying machine learning models in production

Familiarity with ML algorithms (classification, regression) and processes (how to build models, assess their goodness of fit, etc)

Familiarity with agile software development lifecycle (SCRUM, Kanban, etc.)

Previous experience of owning, maintaining and enhancing software data products

Attention to clarity of code, ease of development, and correctness of implementations

Good knowledge of software development best practices including testing, continuous integration, and DevOps tools

STEM-related degree (Bachelor’s, Master’s or Doctorate)

Preferred Requirements

Used Deep Neural Network libraries such as Tensor Flow, especially with Bayesian Neural Networks

Knowledge of cloud systems such as AWS, Azure, GCP and containerisation such as Docker

Experience working with large, real-world datasets

Demonstrated in-depth understanding of product development lifecycle

Experience deploying code into production through CI / CD tools

Knowledge of biostatistics / life sciences / healthcare technology