Data Scientist

Key Responsibilities

• Model Development & Implementation: Design, build, and deploy robust statistical and machine learning models (e.g., for classification, regression, clustering) to predict outcomes and optimise key business metrics.
• Advanced Analytics: Dive deep into large, complex datasets to identify meaningful patterns, trends, and causal relationships that answer critical business questions.
• Data Storytelling & Visualisation: Translate complex analytical findings into clear, compelling narratives and visualisations for both technical and non-technical stakeholders using tools like Power BI, Tableau, or SAS Visual Analytics.
• Data Preparation & Engineering: Take ownership of the data lifecycle, including cleaning, pre-processing, and transforming raw data into high-quality, analysis-ready datasets.
• Experimental Design: Design and execute A/B tests and other experiments to validate hypotheses and measure the impact of new strategies.
• Cross-Functional Collaboration: Partner closely with business analysts, data engineers, and product managers to define project requirements, develop solutions, and integrate them into our operational workflows.
• Continuous Innovation: Stay current with the latest advancements in data science, machine learning, and AI, and champion the adoption of new technologies and methodologies within the team.

Skills & Qualifications

We believe the right candidate will have a blend of technical expertise, business acumen, and strong interpersonal skills.

Required:

• Education: An Honours degree in a quantitative field such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or Econometrics.
• Core Programming: High proficiency in Python (including libraries like pandas, NumPy, scikit-learn) and/or SAS, paired with strong SQL skills for data extraction and manipulation.
• Statistical Foundation: A deep understanding of statistical concepts, experimental design, and modelling techniques.
• Machine Learning: Hands-on experience developing and deploying machine learning models using libraries such as scikit-learn, TensorFlow, or PyTorch.
• Communication: Exceptional ability to articulate complex technical concepts and findings clearly and concisely to diverse audiences.
• Problem-Solving: Strong critical-thinking and analytical skills with a proven ability to solve ambiguous problems with data.

Preferred:

• Education: A Master’s degree or PhD in a quantitative field such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or Econometrics.
• Data Visualisation: Demonstrable experience creating insightful dashboards and reports in tools like Power BI, Tableau, or SAS Visual Analytics.
• Big Data Technologies: Familiarity with distributed computing frameworks like Apache Spark or Hadoop.
• Cloud Platforms: Experience working with data services on cloud platforms such as AWS, Azure, or GCP.