Data Scientist
Summary
Build and deploy ML models (regression, classification, time series) in Python/SQL, engineer features, and monitor performance for business insights.
What Youll Drive
- Design, develop, and maintain statistical, machine learning, and predictive models across regression, classification, clustering, time series, and anomaly detection
- Translate business challenges into analytical frameworks with measurable outcomes
- Perform feature engineering, model selection, and hyperparameter optimisation
- Extract, clean, and transform structured and semiâstructured data from multiple sources
- Conduct exploratory data analysis (EDA) toidentify trends, patterns, and anomalies
- Assess data quality and work with data engineering teams to resolve data issues
- Package and deploy models into production environments (batch or realtime)
- Monitor model performance, drift, and stability over time
- Maintain model documentation, versioning, and retraining strategies
- Partner with stakeholders to understand requirements and deliver insights
- Communicate findings, assumptions, limitations, and recommendations clearly
What Were Looking For
- Bachelors in Data Science, Statistics, Mathematics, Computer Science, Engineering, or related field
- Postgraduate (Masters) advantageous Certifications in data science, machine learning, or cloud platforms beneficial
- Certifications such as CAP, Google Data Analytics, or Microsoft Data Analyst Associate are advantageous
- Python proficiency (pandas, NumPy, scikitâlearn, statsmodels)
- SQL for extraction and analysis
- Experience with large datasets and data warehouses
- Strong understanding of machine learning and statistical modelling
- Knowledge of evaluation metrics and validation techniques
- Experience with data visualisation tools like Power BI, Tableau, matplotlib, seaborn
- Understanding of statistics, feature engineering, model interpretability, bias/variance
- Cloud platforms: Azure or AWS
For more information please call us on
- Design, develop, and maintain statistical, machine learning, and predictive models across regression, classification, clustering, time series, and anomaly detection
- Translate business challenges into analytical frameworks with measurable outcomes
- Perform feature engineering, model selection, and hyperparameter optimisation
- Extract, clean, and transform structured and semiâstructured data from multiple sources
- Conduct exploratory data analysis (EDA) toidentify trends, patterns, and anomalies
- Assess data quality and work with data engineering teams to resolve data issues
- Package and deploy models into production environments (batch or realtime)
- Monitor model performance, drift, and stability over time
- Maintain model documentation, versioning, and retraining strategies
- Partner with stakeholders to understand requirements and deliver insights
- Communicate findings, assumptions, limitations, and recommendations clearly
What Were Looking For
- Bachelors in Data Science, Statistics, Mathematics, Computer Science, Engineering, or related field
- Postgraduate (Masters) advantageous Certifications in data science, machine learning, or cloud platforms beneficial
- Certifications such as CAP, Google Data Analytics, or Microsoft Data Analyst Associate are advantageous
- Python proficiency (pandas, NumPy, scikitâlearn, statsmodels)
- SQL for extraction and analysis
- Experience with large datasets and data warehouses
- Strong understanding of machine learning and statistical modelling
- Knowledge of evaluation metrics and validation techniques
- Experience with data visualisation tools like Power BI, Tableau, matplotlib, seaborn
- Understanding of statistics, feature engineering, model interpretability, bias/variance
- Cloud platforms: Azure or AWS
For more information please call us on