Data Scientist
Data Scientist
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5years
Not Disclosed
Salary Not Disclosed
1 Vacancy
Job Description
EXPERIENCE AND EDUCATION:
Essential Qualifications/Experience:
Strong understanding of machine learning concepts statistical analysis techniques and ability to effectively explain them to both technical and nontechnical colleagues.
Handson experience in programming machine learning and software engineering.
Application of stateoftheart data science and analytical tools and relevant programming languages (python with its Data Science frameworks e/g/ pytorch/tensorflow huggingface langchain autogen pandas/polars pySpark scikitlearn )
Additionally at least 3 of the following items to be met:
Experience with developing optimizing and maintaining AI pipelines
Experience in measuring model performance and assessments
Experience in using and applying the pretrained machine learning models (for example Generative AI) for specific use case
Experience with Version Control MLOps (train package deploy monitor) and CI/CD
Experience with container technologies (e.g. Docker) and orchestration (Kubernetes)
Experience with workflow orchestration (e.g. Airflow Argo etc.)
Experience with REST API development
Experience with frontend development
Applies existing machine learning techniques to new problems and datasets
Evaluates the outcomes and performance of machine learning systems
Identifies issues and recommends improvements to machine learning systems and the data they use. Programming
Designs codes verifies tests documents amends and refactors moderately complex programs/scripts
Applies agreed standards and tools to achieve a wellengineered result
Monitors and reports on progress
Identifies issues related to ML development activities
Proposes practical ML solutions to resolve issues
Collaborates in reviews of work with others as appropriate. Systems and software / ML life cycle engineering
Elicits requirements for systems and software / ML life cycle working practices and automation
Selects systems and software / ML life cycle working practices for components and microservices
Deploys automation to achieve wellengineered and secure outcomes. Systems integration and build
Defines the ML modules needed for an integration build and produces a build definition for each generation of the solution
Accepts completed ML modules ensuring that they meet defined criteria. Data science
Applies existing data science techniques to new problems and datasets using specialised programming techniques.
Evaluates the outcomes and performance of data science models
Identifies and implements opportunities to train and improve models and the data they use. Data engineering
Applies data engineering standards and tools to create and maintain data pipelines and extract transform and load data. Emerging technology monitoring
Supports monitoring of the external environment and assessment of emerging technologies
Contributes to the creation of reports technology road mapping and the sharing of knowledge and insights
Employment Type
Full Time
