Data Scientist

Description:

We are seeking a talented and intellectually curious Data Scientist to join our dynamic team. In this role, you will be at the heart of our decision-making process, transforming vast datasets into actionable intelligence. You will tackle some of our most challenging business problems, using statistical modelling and machine learning to uncover opportunities, optimise processes, and drive strategic growth. If you are passionate about telling stories with data and building solutions that have a real-world impact, we want to hear from you.

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.

Requirements:

• 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.

• 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.

• 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.