Roles and Responsibilities

• Ensure business integration through integrating model outputs into endpoint production systems, where requirements must be understood and adopted relating to data collection, integration and retention requirements incorporating business requirements and knowledge of best practices.
• Build machine learning models from and utilises distributed data processing and analysis methodologies
• Directs the gathering of data for use in Data Science models, ensuring that chosen datasets best reflect the organisations goals
• Performs data pre-processing including data manipulation, transformation, normalisation, standardisation, visualisation and derivation of new variables / features
• Utilises advanced data analytics and mining techniques to analyse data, assessing data validity and usability;
• Review data results to ensure accuracy and communicates results and insights to stakeholders.
• Designs various mathematical, statistical and simulation techniques to typically large and unstructured data sets in order to answer critical business questions and create predictive solutions which drive improvement in business outcomes.
• Drives analytics and insights across the organisation by developing advanced statistical models and computational algorithms based on business initiatives.
• Codes, tests and maintains scientific models and algorithms; identifies trends, patterns and discrepancies in data; and determines additional data needed to support insight.
• Processes, cleanses and verifies the integrity of data used for analysis.
• Use data profiling and visualisation techniques using tools to understand and explain data characteristics that will inform modelling approaches.
• Communicate data information to business with various skill levels and in various roles, presenting trends, correlations and patterns found in complicated datasets in a manner that clearly and concisely conveys meaningful insights and defend recommendations
• Mines data using state-of-the-art methods.
• Enhances data collection procedures to include information that is relevant for building data models.

Requirements

• A graduate degree in a relevant discipline (Mathematics, Actuarial Science, Mathematical Statistics or similar).
• Minimum 4 years proven experience as a Data Scientist.
• Manage aspects of current scorecards/models.
• Maintain and re-calibrate current scorecards and/or predictive models.
• Develop new predictive models.
• Utilises advanced data analytics and mining techniques to analyse data, assessing data validity and usability; reviews data results to ensure accuracy; an communicates results and insights to stakeholders.
• Identifies trends, patterns, relationships, and discrepancies in data and determines additional data needed to support insight. Processes, cleanses, and verifies the integrity of data used for analysis.
• Understanding of and experience of applying machine learning methods Scala (preferred), Python (pySpark), Java (as a precursor to learning Scala) including the key numerical, analytical and machine learning libraries or packages.
• Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS, Kafka, RabbitMQ and Apache Spark.
• Superior applied excel knowledge.

Advantageous

• Exposure to or interest in Big Data technologies or working with large datasets.
• Experience in Machine Learning tools not limited to Elastic Search ML and MS ML Server.
• Experience in AI tools not limited to TensorFlow
• Exposure to large data processing system design including storage, networking, CPU and GPU processing
• Experience with deep learning/neural networks
• Exposure to cloud technologies e.g. Azure, AWS