Job Summary

• Leverage deep data science expertise in advanced statistics, data wrangling, data mining, data analysis, feature engineering &predictive modeling, storytelling, distributed computing & data visualisation, machine learning tools & data intuition to define, build, operationalize & continuously improve data solutions that deliver relevant, quality assured, accurate & commercially impactful data to the business.

Job Description

• Participate in design thinking processes to determine & confirm hypotheses and priority questions / data challenges & related metrics to be solved for 
• Translate business questions to be solved into data requirements & define a data solutions to deliver against these requirements 
• Proactively partner with the data engineering team to refine the data requirements deliver raw data to Data Science teams for interpretation & analysis 
• Design fit for purpose data interpretation & analysis approaches & create customized data models, algorithms, machine learning tools and recommendation engines to achieve the desired business outcomes 
• Use advanced data science skills to mine & interpret data. These include but are not limited to: advanced statistics, data wrangling, data mining, data analysis, feature engineering & predictive modeling, distributed computing, machine learning tools & data intuition 
• Analyse & interpret complex data sets 
• Apply quality assurance frameworks to test model & analysis techniques (e.g. algorithms, models) & overall data quality 
• Apply the testing frameworks to monitor and analyse model performance & data integrity on all data assignments 
• Produce business insights and recommendations based on data analysis & modeling concluded & where relevant with knowledge and experience of e.g. Databricks, Python, Hadoop, Apache Spark  
• Use storytelling and data visualization techniques to maximize impact & deliver a user friendly product to business 
• Contribute to the consolidation of data solutions into viable end products (in the language of business) that can be leveraged on an ongoing basis e.g. dashboards, reports etc. 
• Present data analysis (trends, insights, forecasts) & findings to business & show tangible business impact to be derived from the data science process 
• Facilitate peer reviews & feedback on data solutions 
• Refine data analysis based on business & peer reviews 
• Contribute to the assessments of the effectiveness and accuracy of new data sources & data gathering techniques 
• Promote data literacy with your business stakeholders by sharing best practices and showing tangible business impact & recommendations as a direct result of the the data solutions provided 
• Stay ahead of the curve on data science trends & leading practice data science tools and techniques & transition the organisation to advanced methods for the continuous optimization of data 
• Develop and retain models on customer behavior, retention and segmentation  
• Build algorithms and design experiments to merge, manage, interrogate and extract data to supply tailored reports to colleagues, customers or the wider organization  
• Use machine learning tools and statistical techniques to produce solutions to problems  
• Assess the effectiveness of data sources and data-gathering techniques and improve data collection methods  
• Conduct research from which you’ll develop prototypes and proof of concepts  
• Look for opportunities to use insights/datasets/code/models across other functions in the organisation  
• Stay curious and enthusiastic about using algorithms to solve problems and enthuse others to see the benefit of your work. 
• Development of standardised modelling of behaviour/retention/value in customers 
• Maintenance of dictionaries: codes, dimensions, setting up tables etc 
• Run and compare models on a regular basis 
• Run Cluster specific scenarios to cover specific local issues 
• Business process maintenance (segmentation, primacy etc) 
• Produce appropriate documentation and training material to enable handover to a BAU team. 
• KVD Predictions 
• Using and deriving value from implemented models  

Accountability: Risk & Governance 

• Identify data risks and mitigate these (pre, during & post solution deployment / data delivery) 
• Create business cases & solution specifications for various governance processes (if required) 
• Apply data quality assurance frameworks and tools to guarantee data quality & data integrity (always) for specific data solutions 
• Contribute to risk, governance, compliance & broader regulatory processes as a data science expert (if & when required) 

Accountability: People 

• Coach & mentor other data scientists 
• Conduct peer reviews, testing, problem solving within and across the broader team 

The following degrees and/or subjects may be particularly useful: 

• Data science 
• Industrial engineering 
• Mathematics 
• Statistics 
• Actuarial science 
• Business mathematics and informatics 

Education

• Bachelor’s Degree: Information Technology