A vacancy exists for a Data Scientist within the Data Insights Team, Kazang, in Cape Town.
We are looking for a curious, dependable, and impact-driven Intermediate Data Scientist to step confidently into a role that blends advanced analytics, stakeholder communication, and team contribution. This is an exciting opportunity to play a leading role in shaping our data science environment while delivering high-impact models and insights that inform real business decisions. You’ll work at the intersection of data and strategy—helping to define how data science is applied across the organization.
In this role, you’ll collaborate with Software Engineers, Data Engineers, Data Analysts, Product Owners and decision-makers to scope, build, and deliver solutions using advanced statistical and machine learning models. You’ll be responsible for turning complex data into actionable recommendations and contributing to the design and implementation of data science frameworks using cutting-edge technologies. With a strong passion for modeling and experimentation, you’ll bring a scientific approach to solving business problems in a fast-paced FinTech environment.
Beyond technical delivery, this role is about how you show up in the team – sharing knowledge, offering mentorship, fostering collaboration, and bringing energy and positivity to our day-to-day culture. As a trusted advisor and a core member of the Data & Analytics team, you’ll play a key part in building not just data solutions, but a data-driven, collaborative, and innovative way of working.
Key Responsibilities include, but are not limited to:
- Problem Framing & Business Understanding
- Collaborate with stakeholders to understand business problems and translate them into data science opportunities.
- Define success criteria and hypotheses for modeling efforts.
- Proactively recommend data science applications that add business value.
- Model Development & Analysis
- Build, test, and deploy models for prediction, classification, clustering, or segmentation.
- Use appropriate techniques (e.g., regression, decision trees, time series, NLP, or unsupervised learning) with an understanding of trade-offs.
- Apply rigorous validation and performance testing to ensure robustness.
- Design and implement experiments and conduct A/B testing to evaluate hypotheses.
- Insight Generation & Communication
- Develop statistical models and algorithms to extract insights from complex datasets.
- Interpret model outputs in the context of the business and communicate results to non-technical audiences.
- Create clear, actionable summaries and data stories that drive decisions.
- Highlight not just results, but implications and recommended next steps.
- Collaboration & Culture Building
- Collaborate with cross-functional teams to identify and analyze business problems.
- Contribute positively to team culture through collaboration, energy, and willingness to help others.
- Actively participate in team ceremonies and learning opportunities.
- Support junior team members through code reviews, brainstorming, or informal mentoring.
- Model accountability, open communication, and psychological safety.
- Technical Development & Governance
- Follow reproducible workflows (e.g., version control, environment tracking, documentation).
- Ensure models and codebases are clean, well-documented, and scalable.
- Stay up to date with new techniques and apply relevant advances to your work.
In order to be considered for this position, the following requirements must be met:
- Bachelor’s degree in Computer Science, Statistics, or a related field.
- 2-3 years’ experience working as a Data Scientist in the FinTech industry.
- Proficient in Python, with strong knowledge of key libraries
- Skilled in SQL and experience working with structured data in cloud-based environments (BigQuery exposure is advantageous).
- In-depth knowledge of statistical modelling, machine learning, and data mining techniques.
- Excellent problem-solving skills and the ability to work in a fast-paced environment.
- Ability to independently manage the lifecycle of a data science project from scoping to delivery.
Technical Skills:
- Familiarity with data visualization tools such as Power BI.
- Strong programming skills in Python.
- Experience with cloud data tools (BigQuery and AWS).
- Experience with big data technologies such as Spark is a plus.
- Exposure to MLOps principles (e.g., model tracking, pipelines, CI/CD).
- Experience collaborating with BI teams and integrating data science into dashboards.
- Familiarity with Git, Jira, and Confluence.
- Understanding of experimentation and A/B testing design.
Behavioural Competencies:
- Strong communicator who can simplify complex concepts for business audiences.
- Naturally collaborative, with a desire to support others and share knowledge.
- Detail-oriented but able to keep the bigger picture in mind.
- Brings a positive presence to the team—encouraging, empathetic, and respectful.
- Proactive and curious; always looking for ways to add value.
Performance Expectations:
- Deliver models and insights that directly influence business outcomes.
- Communicate findings in a clear, structured, and compelling way.
- Take ownership of delivery timelines and project accountability.
- Contribute to a supportive, high-performing team environment.
- Share knowledge, support peers, and model a growth-oriented mindset.
- Be recognized by both the team and stakeholders as a reliable, collaborative, and positive contributor.
