Data Scientist

Imizizi

Data analysis, exploration, and feature engineering (EDA)
Strong applied statistics and machine learning foundations
Python-based data science and ML stack (e.g. pandas, NumPy, scikit-learn, PyTorch / TensorFlow)
Data engineering skills: ETL design, batch and streaming data processing
Experience with distributed data systems (e.g. Kafka, Spark or equivalent)
SQL and structured / semi-structured data querying
Experiment design, model evaluation, and validation techniques
Dashboarding, reporting, and data visualisation for insights and decision support
Business problem translation and requirements understanding
Version control and collaborative development (Git)

MLOps practices (model packaging, deployment pipelines, monitoring awareness)
Data governance principles (data quality, lineage, ownership, compliance awareness)
Model evaluation, performance tracking, and drift detection concepts
Cloud-based data and ML environments (Azure / AWS)
Generative AI and LLM-based solution experience (preferred, not essential)
AI agent or advanced prompting familiarity
Experience collaborating with observability and platform engineering teams
Domain-specific knowledge aligned to business use cases

Translate business problems into data-driven and AI-enabled solutions
Perform exploratory data analysis to uncover patterns, issues, and opportunities
Design, build, and maintain data pipelines to support analytics and modelling use cases
Develop, train, evaluate, and iterate on machine learning and AI models
Apply appropriate model evaluation techniques and define success metrics
Support operational data workflows and resolve day-to-day data processing issues when required
Produce clear dashboards, reports, and visualisations for stakeholders
Communicate insights, model behaviour, and recommendations to both technical and business audiences
Collaborate closely with data engineering, AI platform, and observability teams to productionise solutions
Contribute to best practices around data quality, governance, and responsible use of AI

Is outcome-driven, not model-driven
Is comfortable working close to data pipelines and production constraints
Can explain models and uncertainty to business stakeholders
Collaborates naturally with platform, AI engineering, and observability teams
Understands that trust, transparency, and governance are as important as accuracy

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