South Africa: Data Scientist AI/ML (Remote)

About the Role

As a Data Scientist AI/ML, you will play a critical role in translating client business questions into actionable analytic approaches, delivering recommendations that leaders can act on. You will work closely with clients to understand their needs and develop solutions that drive value. Your expertise in machine learning, data analysis, and programming languages such as Python and SQL will be essential in driving business outcomes.

Key Responsibilities

• Translate client business questions into analytic approaches (metrics, segments, hypotheses)
• Deliver recommendations leaders can act on
• Perform data exploration to identify drivers, anomalies, and performance constraints
• Communicate findings clearly to non-technical stakeholders
• Design, run, and analyze experiments (A/B tests) and/or apply causal inference where experimentation is constrained
• Build predictive models with sensible baselines, validation, and error analysis, documenting tradeoffs
• Build LLM/GenAI applications (prompt workflows, tool use, structured outputs) and implement RAG when retrieval improves accuracy
• Create evaluation approaches for LLM systems (test sets, rubrics, automated checks, and human review loops) and iterate based on evidence
• Implement guardrails: PII handling, secure prompting patterns, input/output validation, rate limiting, and fallback behaviors
• Package models and GenAI components into clean services (APIs/batch jobs), with basic CI/CD awareness and production readiness
• Improve/extend data pipelines and feature reliability (freshness, data quality checks, lineage-friendly design)
• Contribute to value measurement: define success metrics, track outcomes, and help clients understand what moved and why

Requirements

• Strong fundamentals in SQL and Python for analysis, transformation, and reproducible work
• Experience delivering at least one end-to-end analytics or ML project (problem framing data model/analysis decision or deployment)
• Working knowledge of experimentation (A/B testing) and common pitfalls
• Comfort explaining statistical reasoning simply
• Exposure to LLM application development (prompting, a small RAG prototype, or integrating an LLM into a workflow)
• ML engineering fundamentals: Git, code reviews, basic testing mindset, and ability to build/maintain a simple API or batch pipeline
• Data quality mindset: proactively validating inputs/outputs and implementing basic checks (freshness, nulls, ranges, duplicates)

Qualifications

• Formal education/certifications in relevant fields (Bachelor’s, LLB, Matric, etc.)

Salary & Benefits

• Not specified

Note: As the original job description did not mention salary or benefits, I have skipped this section. If the information is available, please provide it for inclusion.