Senior Data Scientist MMH250512-12

Role Purpose

Create and deliver data driven solutions that add business value through the use of statistical models, machine learning algorithms, data mining and visualisation techniques.

Requirements

• Postgraduate degree in qualitative discipline’s that include significant exposure to mathematics, statistics and computer science.
• Degree in mathematics, statistics, Engineering, Computer Science or other quantitative field.
• 4+ years’ data science experience and more than 3 years of experience in software/cloud development.
• Extensive experience with at least one programming language (e.g. Python), business analytics software (e.g. SAS) statistical package (e.g. R).
• Experience in database language (e.g. SQL).
• Experience in building and managing big data platforms.
• Deep theoretical understanding of statistical methods and machine learning techniques.
• Extensive software engineering experience and cloud understanding.
• Understanding of Artificial Intelligence solutions and techniques.
• Ability to apply statistical methods and machine learning techniques to solve business problems.
• Formulating business problems to enable statistical modelling.
• Selecting the right statistical tools and techniques for the job.
• Experience translating statistical findings into business recommendations.
• Industry experience in Healthcare and/or Insurance.
• Extensive experience with Amazon Web Services (AWS).
  
  

Duties and Responsibilities

• Identify and develop Predictive and Prescriptive Models to enable better decision making of business.
• Identify, understand and interpret data structures across various databases within the business to facilitate data analysis and continuous monitoring activities.
• Delve for insights in data and processes to help improve the business.
• Contribute to the development of differentiated; superior solutions that meet stakeholder and business requirements through analysis; business requirements gathering and designs validation.
• Ensure product and/or solution design is congruent with the required business specifications through meeting stakeholder requirements timeously.
• Enable the realisation of the financial business benefits accruing including minimisation of operational costs by ensuring that solutions are implemented effectively.
• Model and frame business scenarios that are meaningful and which impact on critical business processes and/or decisions.
• Participate and/or lead discovery processes with business stakeholders to identify problems and opportunities that may be addressed with statistical modelling or machine learning.
• Collaborate with business to define approach to resolution of key business problems or development of new business strategies.
• Contribute to the business by highlighting possible opportunities for process improvement and business value creation.
• Identify and develop the hypothesis testing framework and modelling approach to address the business requirements.
• Identify available and relevant data, potentially leveraging new data collection processes such as social media.
• Make strategic recommendations on data collection and experimental design incorporating business requirements and knowledge of best practices.
• Prepare the data for analysis and modelling, which includes data cleaning, standardisation, transformation, dimension reduction and feature engineering.
• Identify and train suitable models/algorithms to discover patterns and make predictions.
• Extend existing code and develop custom code to implement statistical models, machine learning algorithms and data mining techniques for large datasets in a computationally efficient manner.
• Compare model performance, select the best algorithm for the job and be able to motivate this choice in a non-technical manner.
• Interpret results and translate findings into clear and actionable insights that can be easily validated with the project sponsor.
• Communicate findings 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.
• Assist business users in the use of the models and interpretation of model output.
• Assist with monitoring and reporting on model accuracy after it has been embedded in operations.
• Assist with monitoring and managing cloud expenditure and budget.
• Help lead and mentor team members and collaborate in problem solving.
• Lead projects and manage delivery expectations.
  
  

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