83 Data Scientist jobs in Cape Town

Overview

Data Scientist position available 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.

Duties and Responsibilities

• 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.

Minimum Requirements

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.

#J-18808-Ljbffr

Let's build the future together!

Ikue is a tech start-up with a clear purpose and vision - to provide

telecommunications operators with a superior product to deliver superior

customer experiences.

We know that Customer data is at the heart of hyper personalisation and are

looking for the brightest, most inspiring engineers to deliver our product which

enables data to drive every decision, every communication, and every

customer interaction.

We are building a diverse team, all unified by a desire to unleash the data needed

by marketeers. Creativity is at the core of Ikue and is something we are looking to

further strengthen in 2024! There are no typical profiles, each and every team

member shares our vision and wants to be part of its success.

As a Data Scientist at Ikue you will:

• Interact with customers to understand their AI capabilities, needs and opportunities
• Analyse customer data to identify trends and patterns
• Design and build predictive models and machine learning algorithms
• Design and construct Ikue's AI Studio in collaboration with Product Owners and Engineers
• Ensure predictive models and algorithms remain valid and correctly applied to customer use cases
  
  

You will be rewarded with:

• You will become part of an international environment that embraces diversity and professionalism
• A dynamic and motivated team, with a good sense of humour
• Freedom to take responsibility, grow within the team and ALSO
• Working in a fast-forward company
• Remote work model
  
  

Requirements:

• BSc Computer Science or Engineering
• 3+ years working experience as a Data Scientist or Data Analyst
• Solid grasp of machine learning concepts and approaches (i.e., model types, training, evaluation)
• High expertise in Python and common machine learning frameworks (e.g., Scikit Learn)
• Working knowledge of AWS Sagemaker (AWS technical certification preferable)
• Analytical mind, business acumen, problem-solving aptitude
• Excellent communication and presentation skills

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What do we offer?

• We offer both remote and hybrid work opportunities
• Intellinexus is an SME, therefore you get a lot of exposure to different technologies, solutions and techniques during projects.
• Intellinexus supports career development and growth of its employees, therefore time and money is invested to grow their consultants in their careers.

Overall purpose of the role

This is a data science role focusing on acquiring as many quality clients as possible through performance and brand marketing, in the most cost-efficient way possible.

What you’ll achieve in the first 12 months:

• We have all the data we need to understand, measure and influence client acquisition: Work across the end-to-end client acquisition journey, from off platform to product conversion, to ensure data is created and consumed in a way that enables you to bring them together to fuel your insights and modelling. Ensure data quality and interoperability is owned at source by working with engineers, architects, and marketers across the ecosystem. Success will be measured by the completeness and quality of data, facilitating a comprehensive view of the client acquisition journey.
• We can reliably and predictably attract the targeted segments of the market onto our platform: Collaborate with campaign managers to iterate on marketing campaigns, refine our segments and establish the most cost-effective way to bring prospective clients to our website to evaluate our products. Produce insights, run experiments, and develop predictive models to optimise channel specific campaign performance and cross channel spend. E.g. What budget and channel allocations do we need to hit our client acquisition and cost of acquisition targets for segment X next quarter? Success will be measured in the number of quality clients acquired through each campaign and efficiency of the spend on it (e.g. client LTV / CAC, campaign ROI).
• Prospective clients have all the relevant information and advice they need to understand their needs and build confidence in our products: Work closely with UX designers and user researchers to create the best landing and evaluation journey experience for prospective clients, optimising content and journey’s that are best suited for each segment. Involve user researchers to uncover the why behind the what. E.g. Which web pages contribute most to an acquisition? Do people prefer handholding through our Call Centre Agents or directing their own journey through long form web pages? Success will be measured by conversion of on-platform prospects who enter the qualification funnel and through qualitative surveys about their confidence and experience.
• Once a client decides, the rest of the product onboarding process is so transparent and frictionless, there’s almost no drop off: Work with product houses to identify points in the product application and onboarding journey that cause drop off. Work with user researchers to optimise for maintaining transparency and confidence, while addressing any accessibility needs. E.g. Which parts of the onboarding process require the longest time, how can data and ML be used to reduce or even eliminate it? Success will be measured in completion times and rates in this final step of the process.
• As a team, we always know how well we’re performing and (roughly) why: Create datasets, metric frameworks and visualisations that together provide information-rich stories about our activities and their performance. These stories should cover everything from the digital marketing channels and partners we’re using, to the format and nature of the content (e.g. video vs image, animal photos vs people photos). Leverage computer vision and/or NLP as a service to enrich your data to support this content rich analysis. Success will be measured in the depth of understanding, and minutes it takes, for the team to learn about their key metrics and drivers.

Duties & Responsibilities

• Data Scientists are the people who bring practices from empirical science to life to drive the iterative product development lifecycle. They embed within teams they support, achieving a marriage of minds with their stakeholders and an integration of objectives and workflows. They work across the team with UX researchers, feature engineers, domain experts and business, product, and marketing managers, to create data about user behaviours, discover unmet needs, set metrics that measure performance and design & execute experiments to generate learnings and evidence their success (or lack thereof).
• This is a role where mastery in data wrangling, metric setting, experimentation, machine learning and data storytelling are combined to enable product teams to become learning machines. This requires a thoughtful application of craft across the entire data journey, from data creation to changing our campaigns. Whether it’s creating data or generating learnings, training ML models, setting metrics, or collaborating on the roadmap, data scientists are critical partners to the team they embed in!

Examples of things a data scientist does while embedded in their commercial team:

• Driving data creation: Establish the first principles and facilitate discussions about how to create data about relevant user behaviours and measure success. Design event log schemas and test their implementation.
• Wrangling data autonomously: Wrangle data into the ideal format for the necessary tool (e.g. Python, Tableau, PowerBI), no 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: Identify 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.
• Evolving the learning mindset of the team: Create a culture of insights and learning, where everyone obsesses about their success metrics and are constantly testing and learning to better their performance.

Interested?

Our aim is to help you build a successful career with us

We’re all about building strong, lasting relationships with our employees. We know that you have hopes for your future – your career, your personal development, and for achieving great things. We pride ourselves in helping our employees to realize their worth. Through Intellinexus and its clients, we provide many opportunities for growth and development.

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A leading financial solutions company is on the hunt for a Junior Data Scientist to join their high-performance analytics team.

This opportunity is with an innovative financial solutions provider shaking up the credit and capital space through smart, data-driven decision-making. If youre looking to do more than just crunch numbers, to actually see the effect of your models on real financial outcomes, then this is the place to start. Expect high exposure, hands-on learning, and a team culture that blends curiosity, collaboration, and impact.

Key Responsibilities:

• Explore and analyse large datasets to uncover insights that drive business value.
• Build, refine, and deploy predictive models to improve credit and capital strategies.
• Leverage machine learning and AI tools to enhance efficiency and innovation.
• Work closely with business teams to translate data into actionable results.

Job Experience and Skills Required:

• Degree in Data Science, Mathematics, Actuarial Science, Statistics, or another quantitative field.
• Up to 1 year in a data-focused role (bonus points if its in fintech, credit, or financial services).
• Proficient in Python, R, and SQL, with an understanding of machine learning and AI techniques.
• Analytical thinker with a curious streak, eager to learn and make an impact from day one.

Apply now!

Were looking for a data scientist to join our technology team. You'll leverage cutting-edge cloud technology and work on exciting challenges that directly impact business decisions and user experiences. The position is based at our Cape Town or Randburg offices. WHAT YOULL DO Machine learning & automation Design, build, test and optimise predictive models that deliver automated business intelligence Develop sophisticated algorithms to solve complex business challenges Deploy and monitor ML models in production environments with proper versioning and tracking MLOps & model lifecycle management Track model performance, detect drift and manage model retraining workflows Containerise ML applications and manage model versioning across environments Partner with cross-functional stakeholders to identify, scope and solve critical business problems Create automated reporting systems and interactive dashboards that empower data-driven decision making Monitor platform performance and establish key performance metrics Analytics & insights Analyse diverse data sources including custom analytics, paywall metrics, and web analytics to uncover actionable business insights Conduct deep-dive user behaviour analysis to enhance UX and drive engagement Data engineering & pipeline development Build and maintain robust data pipelines for ingesting, processing and transforming large datasets Ensure data quality and implement validation checks across data workflows Design efficient ETL/ELT processes to support analytics and ML initiatives EDUCATION & EXPERIENCE Honours degree (minimum) in Data Science, Mathematics, Statistics, Engineering or related field 3 years of hands-on Python development experience Proven experience with big data technologies and cloud platforms Technical expertise Development Tools: Git version control, Jupyter Notebooks, Docker ML Frameworks: scikit-learn, PyTorch, TensorFlow, LightGBM, XGBoost, Pandas Data Engineering: SQL, DAG orchestration tools, data pipeline design, ETL/ELT processes Statistical Methods: Linear/logistic regression, statistical analysis techniques Recommendation Systems: Collaborative filtering, content-based and hybrid models Tree-Based Methods: Random Forests, decision trees, gradient boosting Advanced Techniques: Clustering algorithms, Natural Language Processing (bag-of-words, word embeddings, transformer models) Model Deployment: Production deployment, A/B testing, model monitoring and maintenance Data engineering skills Strong SQL proficiency and database design principles Experience with data warehousing concepts and dimensional modelling Knowledge of data quality frameworks and validation processes Understanding of streaming vs. batch processing architectures Mindset Curiosity and eagerness to learn emerging technologies, platforms and methodologies Problem-solving approach with attention to detail and business impact Strong collaboration skills for working across engineering and business teams Given the employment equity policy of Media24, preference will be given to suitable candidates from the designated groups.

We are looking for a Data Scientist with a strong academic background and proven industry experience to join our clients growing team. Be part of a forward-thinking team using advanced data science techniques. This is an opportunity to work on impactful projects across industries that offers growth, learning, and career development opportunities. Apply now and take the next step in your Data Science career Requirements: · Degree in Data Science / Computer Science / Statistics / Mathematics (essential) · Minimum of 3 years work experience in a Data Scientist role · Proficiency in Python / R and relevant data science libraries · Strong knowledge of machine learning techniques and predictive modeling · Experience working with large datasets and SQL databases · Excellent analytical, problem-solving, and communication skills Duties: · Develop and implement data models and algorithms to solve business problems · Analyse large, complex datasets to extract actionable insights · Collaborate with stakeholders to understand requirements and deliver data-driven solutions · Present findings and recommendations to both technical and non-technical audiences Should you meet the requirements for this position, please email your CV to . You can also contact the team on XXX-XXX or alternatively you can visit our website Should you not hear from us within 3 days, please consider your application unsuccessful.

About the Role We’re looking for a data scientist to join our technology team. You’ll leverage cutting‑edge cloud technology and work on exciting challenges that directly impact business decisions and user experiences. The position is based at our Cape Town or Randburg offices. Responsibilities Design, build, test and optimise predictive models that deliver automated business intelligence Develop sophisticated algorithms to solve complex business challenges Deploy and monitor ML models in production environments with proper versioning and tracking Track model performance, detect drift and manage model retraining workflows Containerise ML applications and manage model versioning across environments Partner with cross‑functional stakeholders to identify, scope and solve critical business problems Create automated reporting systems and interactive dashboards that empower data‑driven decision making Monitor platform performance and establish key performance metrics Analyse diverse data sources including custom analytics, paywall metrics, and web analytics to uncover actionable business insights Conduct deep‑dive user behaviour analysis to enhance UX and drive engagement Build and maintain robust data pipelines for ingesting, processing and transforming large datasets Ensure data quality and implement validation checks across data workflows Design efficient ETL/ELT processes to support analytics and ML initiatives Technical Expertise Git version control, Jupyter Notebooks, Docker scikit‑learn, Py Torch, Tensor Flow, Light GBM, XGBoost, Pandas SQL, DAG orchestration tools, data pipeline design, ETL/ELT processes Linear/logistic regression, statistical analysis techniques Collaborative filtering, content‑based and hybrid recommendation systems Random Forests, decision trees, gradient boosting Clustering algorithms, natural language processing (bag‑of‑words, word embeddings, transformer models) Production deployment, A/B testing, model monitoring and maintenance Data Engineering Skills Strong SQL proficiency and database design principles Data warehousing concepts and dimensional modelling Data quality frameworks and validation processes Streaming vs. batch processing architectures Mindset Curiosity and eagerness to learn emerging technologies, platforms and methodologies Problem‑solving approach with attention to detail and business impact Strong collaboration skills for working across engineering and business teams Education & Experience Honours degree (minimum) in Data Science, Mathematics, Statistics, Engineering or related field 3+ years of hands‑on Python development experience Proven experience with big data technologies and cloud platforms Given the employment equity policy of Media24, preference will be given to suitable candidates from the designated groups. #J-18808-Ljbffr

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 Fin Tech 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 Fin Tech industry. Proficient in Python, with strong knowledge of key libraries. Skilled in SQL and experience working with structured data in cloud‑based environments (Big Query 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 (Big Query 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. #J-18808-Ljbffr

About our client: An international and highly regarded organisation dedicated to “doing good” for society, they are renowned for their ethics, upliftment programs and while their tech environment is leading edge, this organisation is ideal for someone who wants to make a real difference to the greater good of society. While this organisation has deep funding and you will earn well, it is best suited to those who have a strong social conscience and humanitarian interests. What you will be doing: Shape a dynamic data-driven culture for impactful decision-making.Empower teams with cutting-edge data governance and reporting.Be a pioneer in implementing innovative data collection solutions.Infuse reports with engaging visuals, tailored for strategic insights.Collaborate on user-friendly templates for an enriched experience.Drive the narrative of data intelligence aligned with impact metrics.Champion continuous improvement in data quality and security.Contribute to a vibrant and insightful reporting ecosystem. What you need: Bachelor’s degree in Data or Information Sciences or equivalent.Ideally more but no less than 3 years demonstrated work experienceStrong attention to detail and organisational skills.Results-oriented and passionate about data-driven decision-making.Proficient in Excel, analytics, and impact measurement frameworks.Effective written and verbal communication.Ability to build databases, dashboards, and ensure data security.Initiative-driven, excels in a fast-paced environment.Advanced Excel and statistical analysis background.Strong interest in AI, Machine Learning platforms, and systems architecture.Capable of building end-to-end Business Intelligence Solutions. Job ID: J PS Even if you feel you don’t have all the skills listed or if this spec isn’t what you are looking for, feel free to send your CV as we probably have other opportunities that could interest you. For a more comprehensive and updated list of opportunities that we have on offer, do visit our website -