Data Analyst

**Business Unit**:Discovery Health**Function**:Data Analysis**Date**:25 Oct 2024

Discovery Health

Data Analyst
- About Discovery
- Discovery’s core purpose is to enhance and protect people’s lives. It does this through breakthrough product designs that harness incentives to encourage people to make healthier lifestyle choices. Healthy behaviour leads to lower claims, higher margins, and lower lapses. These savings are shared with our clients which in turn leads to a healthier society, improved productivity, and a reduced healthcare burden. One of Discovery’s core assets is its large and diverse data, covering health, wellness, driving, investments, and life insurance. This forms the basis for our shared value model, along with innovation, risk management and operational efficiency improvements. Discovery’s energetic and motivated analytical teams make this happen.
- About the Data Science Lab
The Data Science Lab applies predictive analytics, machine learning, big data, and operations research skills to run and to support key projects for the Discovery Group and for the individual Discovery business units, including the health, life, and short-term insurance businesses. We work across operational, clinical, wellness, financial, customer service, sales, and behavioural science areas. We use and create state-of-the-art tools and work with terabytes of structured and unstructured data within a big data environment.- About the Position
- The key purpose of the Data Analyst role is to design and implement analyses, and communicate findings and insights to support the work of our data science team within the Group Data Science Lab. This team focuses on the development and evaluation of Natural Language Processing (NLP) and Large Language Model (LLM) systems. The role requires working with both structured and unstructured data, collaborating closely with a team of data scientists and engineers, and fostering strong relationships with operations and business stakeholders to ensure alignment between the data science work and business objectives.
- Responsibilities include
- Query and analyse structured and unstructured data using SQL, Excel, and ideally Python or R.
- Design, implement, and communicate analyses that inform the development of NLP and LLM systems, including modelling decisions, system design, and rollout decisions.
- Design, implement, and communicate analyses that help the team and stakeholders measure the operational and business impact of our work.
- Assist in defining, developing, and implementing evaluation metrics, and analyse trends in these metrics.
- Collaborate with data scientists on statistical analyses to measure and prove impact and value.
- Present analysis findings clearly, providing actionable insights for both data scientists and business users.
- Design comparative analyses to help us understand the operational impact (e.g., interaction volumes, handling time, survey scores) of our work.
- Conduct text analysis using manual methods, NLP techniques, and LLMs.
- Aggregate and synthesize data to produce accurate weekly/monthly reporting for team and senior stakeholders.
- Engage with stakeholders to contextualise insights within an operational context.
- Personal Attributes and Technical Skills
- Expertise in data manipulation, especially using Excel and SQL for structured data; experience with Python or R is advantageous.
- Strong analytical, statistical, and problem-solving skills. Experience in statistical modelling, data mining, feature engineering, and machine learning is advantageous.
- Ability to formulate problem statements and develop actionable plans.
- Skills in working with text data analysis, NLP techniques, and LLMs are advantageous.
- Able to convey data insights and recommendations visually, verbally, and in writing.
- Excellent planning, organizational, and time management skills, with attention to detail.
- Self-motivated and proactive, capable of working independently and without supervision when necessary.
- Team player with the ability to collaborate effectively and balance multiple priorities.
- High levels of resilience, enthusiasm, energy, and drive.
- Keen to learn and grow as a data science professional.
- Education and Experience
- Relevant quantitative degree in Statistics, Mathematics, Computer Science, Data Science, or similar, or equivalent experience.
- 1-2 years of work experience in an analytical environment.
- Work experience in operational or contact centre environments is advantageous.
- EMPLOYMENT EQUITY

The Company’s approved Employment Equity Plan and Targets will be considered as part of the recruitment process. As an Equal Opportunities employer, we actively encourage and welcome people with various disabilities to apply.