Sr Data Scientist, Digital Products & Experience at Sanlam Group

Position Overview

• As a data scientist your work is a canvas for change, painted with the data you explore and the insights you uncover. You’re not just joining a team; you’re embarking on a mission to reshape the financial landscape for all Africans, making tomorrow not just different, but better.
• Turning data into pathways, obstacles into stepping stones, and potential into reality. Together, we can build lasting financial confidence across Africa, one insight at a time.
• This is a data science role embedded in a team focused on creating a world-class digital financial platform that is insights driven and behaviorally aware, through curated, personalised and inclusive products and customer experience. 
• This is a data science role focusing on driving continuous improvements to our retail credit products, both digital and financial.

Qualification and Experience

• 5+ years’ experience as a data scientists with at least 2 in a similar product data science capacity
• Track record of high impact and investing in your own development.
• Data science experience in a product focused role specifically
• Experience working with Tableau or PowerBI. Competent in python and SQL.
• Experience with digital products in an app environment
• Experience with cloud technologies like Snowflake and SageMaker
• Experience designing and analyzing client research

What you’ll achieve in the first 12 months

• Wrangling data autonomously: Wrangle data into the ideal format for the necessary tool (e.g. Python, Tableau, PowerBI), no matter how raw and unstructured it and what lake/lakehouse it’s stored in.
• Setting team metrics: Define holistic measurement frameworks that enable the team to explore their performance holistically and work with the team to identify and track the metrics that measure success
• Producing insights: Identity loosely defined commercial problems and produce meaningful insights that guide iterations of the teams products and go-to-market campaigns
• Developing ML models: Structure commercial problems into prediction problems by clarifying both what is being predicted and how that enables us to change our products or campaigns to obtain a better result. Develop and implement ML models for the highest priority prediction problems, ensuring outstanding model and product performance.
• Carrying out controlled experiments: Establish the learning goals of their team, crafting hypothesis statements and learning roadmap wherever needed. Design, execute and draw correct conclusions from experiments by confirming integrity of experiment, avoiding common pitfalls, and executing frequentist or Bayesian A/B analysis.
• Segmenting the client base: Using rule and unsupervised learning-based approaches to segment the client base so we can develop a deeper understanding of customers and build better products for them.
• Creating impactful visualisations: Produce data and interactive visualisations that enable the whole team to address their recurring reporting and insight needs, without your direct support.
• Partnering for impact: Partner with stakeholders to make sure they gain the confidence they need to drive changes in the product roadmap or ask for further insight.