Research : Machine Learning Data Scientist X3 (9864)

The South African Revenue Service; Johannesburg; On-site; ZAR 60 000 - 100 000; Full time

Job summary

An established industry player is seeking a skilled Machine Learning Data Scientist to provide expert advice on developing innovative statistical products. This role involves utilizing advanced machine learning techniques to enhance operational efficiencies and support organizational compliance. The ideal candidate will have extensive experience in quantitative fields, a deep understanding of machine learning algorithms, and the ability to communicate complex concepts effectively. Join a forward-thinking organization that values creativity and innovation in tackling real-world challenges through data science.

Qualifications

• 10-15 years of experience in a quantitative field with specialization in ML.
• Fluency in Python and R, with strong SQL skills.

Responsibilities

• Provide expert guidance on ML/AI product design and implementation.
• Analyze data to improve operational efficiencies and detect non-compliance.

Skills

Statistical Analysis

Machine Learning

Python

R

SQL

NLP Techniques

Data Analysis

Education

Honours/Postgraduate Diploma in a quantitative field

Bachelor's Degree in a quantitative field

Master's Degree

Tools

AWS

Azure

GCP

IBM Cloud

DynamoDB

Job description

Job title : Research : Machine Learning Data Scientist x3 (9864)

Job Location : Gauteng, Johannesburg Deadline : May 30, 2025 Quick Recommended Links

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Job Purpose

• To provide expert advice and guidance in the design, implementation and validation of statistical machine learning / artificial intelligence products, be innovative in blending algorithms with the expertise of the organisation, to best serve the needs of SARS customers and support the organisation’s strategic intent of voluntary compliance.
• Use algorithms to improve operational efficiencies as well as detect non-compliance, conduct research and keep abreast of latest tools and models to be applied in resolving the organisation’s problems.

Education and Experience

Minimum Qualification & Experience Required

• Honours / Postgraduate Diploma (NQF 8) in a quantitative field e.g. Actuarial Science, Statistics, Physics, Computer Science, Data Science, Machine Learning or similar AND 10-12 years' experience in a similar environment of which 3-4 years at a specialist level
• Bachelor's Degree / Advanced Diploma (NQF 7) in a quantitative field e.g. Actuarial Science, Statistics, Physics, Computer Science, Data Science, Machine Learning or similar AND 12-15 years related experience of which 3-4 at a specialist level
• A Master’s degree would be advantageous.

Minimum Functional Requirements

• Skilled in statistics or machine learning.
• Able to read and digest academic papers to think creatively about our data and provide guidance on what to build.
• Fluency in a coding language such as R, Python or similar.
• Excellent written and verbal communication. Able to articulate concepts effectively in different ways dependent on the audience – non-technical, non-ML technical, ML / DS practitioners.
• Advanced level of experience with relational databases and SQL knowledge (5+ years).
• Some experience with non-relational databases e.g. DynamoDB (1+ years).
• Experience with different data format types e.g. JSON, Parquet, CSV, Pickle, XML (2+ years).
• Some experience with unstructured data and NLP techniques is desirable (not only ChatGPT e.g. Spacy, Hugging Face) (1+ year).
• Intermediate experience running end-to-end machine learning or research projects (must have been involved in operationalizing ML / AI projects).
• Experience with streaming data is an advantage.
• Cloud experience on AWS, Azure, GCP, IBM Cloud is an advantage.
• Experience with foundational models (deploying as-is, using RAG or fine tuning) is an advantage.

Job Outputs : Process

• Analyse and make recommendations about improvements to specialist systems, procedures, policies and practices.
• Develop multiple practices in alignment with operational policy and procedural frameworks, supporting tactical development and excellence.
• Integrate business information, compare, analyse and produce reports to identify trends, discrepancies and inconsistencies for decision-making purposes.
• Stay up to date with DS / ML / AI techniques and trends and supporting architecture trends in the domain.
• Think creatively about the organisations’ data and provide guidance on what to build (using skills and innovative mindset to spot opportunities).
• Share best practices and learnings to the team and the wider data specialists across the organisation.
• Analyse and critique Machine Learning models of teammates to continuously improve our artefacts.
• Expose Machine Learning outputs via APIs or similar, to stakeholders to use.
• Define and build dashboards and analyses / reports that track KPIs, experiments and new initiatives.