DATA SCIENTIST
MIDRAND
Email cv’s to: ****@*****.***

Minimum Education
• Degree or relevant qualification with quantitative subjects or relevant professional certification
• Honours Degree in Data Science or AI or Computer Science or Econometrics or Engineering or Statistics or Actuarial Science or similar or relevant professional certification
Minimum Experience
• 3 years’ experience in data science or analytics or academic or financial role.
• 5 years’ experience in data science or analytics or academic or financial role.
Technical (COMP)
• Attention to detail
• Business Acumen
• Communication Skills (verbal & written)
• Computer programming (and ability to learn relevant computer languages quickly)
• Data driven
• Data visualisation skills
• Deadline driven
• Facilitation Skills
• Financial modelling skills
• Inter personal skills
• Knowledge of banking and working domain specific knowledge (e.g. credit, collections, HC or sales) for which problem is being solved
• Leading a small team or taking a leading role in certain projects
• Machine learning techniques
• Mathematical modelling
• Matlab/Octave and/or Java and/or SAS and/or C and/or C++ and/or TensorFlow and/or AWS and/or NoSQL and/or Hadoop/Hive/Pig and/or Azure and/or Spark and/or Julia and/or Unix Shell or similar (Advantange)
• Presentation Skills
• Problem solving
• Python and/or R or similar
• Report writing skills
• Results driven
• SQL Skills
• Statistics and mathematics
• Strong Analytical & financial modelling skills
• Understanding and knowledge of: Business policies and procedures. Legislation
Overview:
1 - Stakeholder Engagement and Expectation Management
1.Engages positively within data science team and with external stakeholders
2.Understands and delivers on agreed deliverables and business owner’s objectives
3.Keeps stakeholders informed of progress
4.Seeks assistance to overcome obstacles as they may arise
5.Ensure solutions developed can be practically implemented
2 - Data wrangling and data preparation
1.Retrieve data from identified data sources (SQL, SAS, unstructured data sources)
2.Validate, clean and filter data against identified criteria
3.Package data into correct format for further analysis or delivery
4.Create data sets as required for ad hoc analysis
5.Enhance data documentation where possible
6.Document data wrangling steps clearly
3 - Analysis, reporting, data visualisation and communicating results
1.Ensures high quality presentations appropriate for diverse audiences to make informed decisions
2.Analyse data and verify results in terms of requested information by applying suitable standard and advanced univariate and multivariate statistical techniques
3.Document process followed to determine findings from data retrieval to final outcomes
4.Prepare a written report of findings for distribution to direct manager for final approval
5.Prepare presentation for feedback to Exco or identified stakeholders
6.Communicate findings to relevant stakeholders in order to adopt recommendations
4 - Statistical Model building and Machine Learning model building
1. Develop and maintain an understanding of relevant business processes
2. Review current processes for business opportunities by means of appropriate statistical models
3. Build statistical models to drive business strategies
4. Understand and interpret specifications of the model from the business by liaising with different stakeholders
5. Prepare a project plan in terms of requirements for completion of model
6. Prepare and analyse data
7. Develop a descriptive or predictive mathematical or statistical model making use of available statistical tools
8. Validate the model in terms of requirements
9.Communicate the models performance (verbal and written) to relevant stakeholders
10.Prepare a presentation of results for presentation to Exco or identified stakeholders for final approval
11. Document specifications and process of model roll out working with Strategy Manager and bureau for deployment
12.Test and validate functionality of the implementation of the model in terms of requirements
13.Assist in regression analysis by creating new score cards
14.Review current modelling methodologies in respect to international best practices
5 - Model monitoring and refinement
1.Monitor models to determine predictive power is maintained on an ongoing basis
2.Analyse data in respect of models where outcomes are not in line with expectations to identify causes for variances
3.Identify and recommend suitable solutions in writing with evidence of the identified problem
4.Implement approved solution in terms of recommendation
6 - Change Management
1.Write appropriate specifications need to implement change process or recommendation after obtaining necessary approval from relevant committee
2.Oversee the changes to be done in consultation with Business Analysts
3.Test and validate functionality of the implementation process
4.Obtain final sign of implementation
5.Maintain complete documentation of any follow up changes
6.Enable audit and validations to be performed
7 - Data ethics, governance and privacy
1.Understands legislation and comply with it, e.g. data privacy rules complied with
2.Ethics to be considered at all stages of work
3.Avoid unfair discrimination
8 - Mentoring and training
1.Assist team members with new tasks in respect of unfamiliar aspects of the job
2.Train team members in respect of technical aspects and business aspect of the job as well as for succession planning
9 - Research
1.Enhance existing models
2.Alternative models, approaches and methodologies
3.Find new data sources
4.Find new ways of utilising and absorbing diverse data sources for business problems and to maximise value from data
10 - Treating Customers Fairly and Compliance
1.Create and maintain productive relationships with internal and external clients by providing advice and assistance
2.Create understanding of the ‘real’ versus ‘perceived’ need through experience and expertise while complying with company policies, legislation and regulations
3.Keep the client informed about progress through written communication, telephone communications, and/or face-to-face meetings
4.Build a positive image by exceeding client expectations at all times
5.Treat internal and external customers fairly at all times
11 - Management of Resources
1.Manage and develop subordinate(s): Performance management in terms of contracting, reviews and poor performers. Training and development. Employee relations
2.Manage people efficiencies through leave management, headcount budget, fixed term contracts, staff movements, secondments, staff utilization
3.Take appropriate disciplinary measures as required
4.Facilitate induction of new staff within one month of joining the organization
Nature 1:
Wrangle data, analyse data, develop machine learning models and develop data-driven solutions to key stakeholders of business areas. May lead small team. Advanced competency level demonstrated

Motivation:
KEY PERFORMANCE AREAS
Behavioural (COMP)
• Ability and willingness to learn and try new approaches
• Analytical thinking
• Coping with Pressures and Setbacks
• Creating & Innovating
• Curiosity
• Delivering Results and Meeting Customer Expectations
• Enjoys solving difficult problems
• Integration/Holistic thinking
• Leading and Supervising
• Logical reasoning
• Planning and Organising (Prioritising)
• Presenting and Communicating Information
• Teamwork