Data Scientist AI/ML

Role Overview
The company helps clients become data-driven by applying data + technology to create real-time,
actionable visibility into business performance and to build lasting analytics capability. This role will help
expand delivery capacity for pragmatic, production-grade analytics, ML, and GenAI solutions, so client
teams can move from "interesting insights" to deployed capabilities with measurable value.

What youll do

• Translate client business questions into analytic approaches (metrics, segments, hypotheses), then 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 analyse experiments (A/B tests) and/or apply causal inference where experimentation is constrained.
• Build predictive models (e.g., risk/propensity/forecasting) with sensible baselines, validation, and error analysis, document 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 (the company emphasizes value measurement as part of delivery).
• Work within a structured engagement approach (Discover Review Implement Value Evolve), producing lightweight deliverables clients can run with.

Role Expectations

• Strong fundamentals in SQL + Python for analysis, transformation, and reproducible work (notebook + production-friendly code).
• 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: you proactively validate inputs/outputs and can implement basic checks (freshness, nulls, ranges,
  
  https://www.jobplacements.com/Jobs/D/Data-Scientist-AIML-1262240-Job-Search-02-13-2026-04-09-59-AM.asp?sid=gumtree