GIC is a leading P3 infrastructure developer specialising in design, build, and financing (DBF) of infrastructure projects in Africa. Our P3 private public partnership with governments and the private sector…
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Senior Data Scientist (with Full Stack Development Expertise)
Lead design and development of end-to-end machine learning models.
Own data science lifecycle: from exploration and experimentation to production deployment.
Work with engineers and analysts to build pipelines and intelligent systems.
Drive AI/ML use cases, including NLP, forecasting, anomaly detection, and optimization.
Mentor junior team members and help establish best practices.
Communicate findings and strategic recommendations to leadership.
Assess, integrate, and extract data across multiple databases (structured and unstructured) to create usable datasets for analytics, dashboards, and business reporting.
Design and implement automated data extraction and transformation workflows to support real-time reporting needs.
Collaborate with IT and business units to understand data architecture and improve data accessibility and governance.
Required Skills & Experience
5+ years in data science or related field.
Expert proficiency in Python (e.g., Scikit-learn, PyTorch/TensorFlow, Statsmodels).
Strong SQL and experience with data engineering workflows.
Proven ability to translate business problems into data science solutions.
Hands-on experience with production ML pipelines and CI/CD.
Familiarity with Azure cloud platforms.
Excellent storytelling and stakeholder management skills.
Proficiency in full-stack development (e.g., JavaScript/TypeScript, Node.js, React, or similar), enabling development of internal tools or dashboards where necessary.
Strong understanding of relational and non-relational databases (e.g., SQL Server, MongoDB, PostgreSQL).
Demonstrated experience in integrating backend services with frontend applications for end-to-end data solution
Additional Skills
Experience with LLMs (e.g., OpenAI, Hugging Face Transformers).
Background in building recommendation engines, predictive maintenance, or fraud detection.
Familiarity with MLOps tools (e.g., MLflow, Kubeflow).
Experience working with unstructured data (text, image, audio).
"Experience developing and maintaining business intelligence dashboards (e.g., Power BI, Tableau, custom-built)."
"Knowledge of DevOps tools and practices for managing infrastructure supporting analytics workloads."
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