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Job Purpose:
- To analyse large volumes of structured and unstructured data, develop predictive and classification models, and create data-driven solutions aligned with business needs. The role also includes preparing and transforming data for modelling, deploying machine learning models, and collaborating across teams to drive innovation and deliver actionable insights.
REQUIREMENTS
Minimum education (essential):
- Bachelor’s or Honours degree in Data Science, Statistics, Computer Science, Applied Mathematics, or related field.
Minimum applicable experience (years):
- 2–4 years of experience in a data science role, ideally in a fast-paced or product-led environment.
Key Responsibilities:
Data Analysis & Modelling
- Analyse large volumes of structured and unstructured data to discover trends, patterns, and actionable insights.
- Develop, train, and validate predictive and classification models (e.g., regression, decision trees, clustering, recommendation systems).
- Work with time-series, transactional, and behavioural data to develop advanced insights.
Model Development & Deployment
- Implement machine learning models using Python, R, or similar languages.
- Collaborate with engineering teams to productionize models into applications and platforms.
- Monitor and tune deployed models for performance and accuracy over time.
Data Preparation & Feature Engineering
- Work with data engineers to define and extract relevant datasets.
- Perform cleaning, transformation, feature extraction, and normalization of raw data.
- Conduct exploratory data analysis (EDA) to support modelling or reporting.
Business Alignment
- Work with business stakeholders to understand challenges and opportunities.
- Translate business problems into data science solutions and communicate results clearly to non-technical teams.
- Present findings through dashboards, visualizations, or reports.
Collaboration & Best Practices
- Work in cross-functional Agile teams to deliver iterative value.
- Document methodologies, models, and code for team reusability.
- Contribute to the continuous improvement of data science practices and frameworks.
Skills and Knowledge (essential):
Technical Skills
- Proficient in Python (preferred) or R for data science and machine learning tasks.
- Experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
- Solid SQL skills; familiarity with big data tools (e.g., Azure Synapse Analytics, Fabric) is a plus.
- Experience with data visualization tools (e.g., Power BI, Tableau…).
- Experience working with APIs, cloud environments (Azure, AWS, or GCP), and Git-based workflows.
Soft Skills
- Strong problem-solving skills and a proactive mindset.
- Good communication and presentation abilities.
- Team player with the ability to work independently when needed.
- Attention to detail and a strong analytical mindset.
