76 Data Scientist jobs in Cape Town

• 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 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
  
  

• 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

SWATX is looking for a talented and driven Data Scientist to join our dynamic team. In this role, you will leverage your expertise in data analysis, machine learning, and statistical modeling to extract insights from complex datasets and drive data-driven decision making within the organization. You will work closely with business stakeholders to identify opportunities for leveraging data to improve products, services, and overall business performance.

Responsibilities:

• Analyze large datasets to identify trends, patterns, and insights that can inform business strategies
• Develop and implement predictive models and machine learning algorithms to solve complex business problems
• Collaborate with cross-functional teams to understand data requirements and provide analytical solutions that meet business needs
• Design and conduct experiments to test hypotheses and validate results
• Communicate findings and recommendations to stakeholders through presentations and reports
• Continuously monitor and improve model performance through regular updates and refinements
• Stay updated with the latest advancements in data science, machine learning, and AI technologies to apply best practices in your work
• Build and maintain data pipelines to ensure the availability of data for analysis and reporting
• Work with data engineering teams to ensure data is collected and stored effectively for analysis

Requirements:

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field
• Proven experience as a Data Scientist or in a similar analytical role
• Strong programming skills in Python, R, or similar languages
• Proficiency in SQL and experience with relational and non-relational databases
• Experience with machine learning libraries and frameworks (e.g., Scikit-learn, TensorFlow, Keras)
• Knowledge of data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn) for communicating results
• Understanding of statistical concepts and methodologies, and experience applying them to real-world scenarios
• Excellent analytical and problem-solving skills
• Strong communication skills, both verbal and written, in English and Arabic

Preferable Certificates:

• Certified Data Scientist (CDS)
• Microsoft Certified: Azure Data Scientist Associate
• Google Cloud Professional Data Engineer
• SAS Certified Data Scientist

Overview

Turn sports data into winning strategies with advanced algorithms and machine learning

Cape Town | R1 200 000 - R1 300 000 per annum | 08:00am - 17:00pm

• Design, develop, and evaluate signal processing algorithms to deliver insights for players, coaches, and broadcasters
• Build and refine algorithms under the guidance of senior team members
• Develop and maintain documentation of the data science architecture and algorithms
• Partner with sports scientists to collect and validate sensor data
• Develop and train machine learning models to provide predictive insights into team strategies

• BEng/BSc or higher in Electronic Engineering, Mathematics, or Computer Science
• Strong knowledge of signal processing concepts (filtering, Nyquist frequencies, time-frequency transformations)
• Understanding of algorithm performance evaluation techniques (e.g., receiver-operating-curves)
• Experience with machine learning algorithms (regression, decision trees, SVMs, neural networks)
• Proficiency in Python, MATLAB, Octave, or R for data analysis and simulations
• Excellent time management and attention to detail
• Hands-on, technically curious, and an effective communicator
• Strong interest in sports trends and analytics

• 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.

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.

• 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.

• 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!

• 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.

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.

• • Data Scientist

    Turn sports data into winning strategies with advanced algorithms and machine learning
    Cape Town | R1 200 000 - R1 300 000 per annum | 08:00am 17:00pm

    About Our Client
    Our client is an innovative sports technology company pushing the boundaries of performance analytics. By combining cutting-edge data science with elite-level sports expertise, they provide actionable insights that empower coaches, players, and broadcasters. The team is driven by a passion for precision, innovation, and unlocking new competitive advantages through data.

    The Role: Data Scientist
    As a Data Scientist, you will be responsible for designing, developing, and evaluating advanced algorithms and models that transform raw sensor data into meaningful insights. This role involves close collaboration with engineers and sports scientists to support predictive analytics that inform strategies in elite sports.

    Key Responsibilities

    • Design, develop, and evaluate signal processing algorithms to deliver insights for players, coaches, and broadcasters

    • Build and refine algorithms under the guidance of senior team members

    • Develop and maintain documentation of the data science architecture and algorithms

    • Partner with sports scientists to collect and validate sensor data

    • Develop and train machine learning models to provide predictive insights into team strategies

    About You

    • BEng/BSc or higher in Electronic Engineering, Mathematics, or Computer Science

    • Strong knowledge of signal processing concepts (filtering, Nyquist frequencies, time-frequency transformations)

    • Understanding of algorithm performance evaluation techniques (e.g., receiver-operating-curves)

    • Experience with machine learning algorithms (regression, decision trees, SVMs, neural networks)

    • Proficiency in Python, MATLAB, Octave, or R for data analysis and simulations

    • Excellent time management and attention to detail

    • Hands-on, technically curious, and an effective communicator

    • Strong interest in sports trends and analytics

• Develop and implement machine learning models to support product development and business decision-making.
• Analyse large and complex datasets to identify patterns, trends, and actionable insights.
• Collaborate with Software Developers, Product Managers, and Data Engineers to design data-driven solutions.
• Evaluate and improve model performance through rigorous testing and optimization.
• Communicate findings and recommendations to both technical and non-technical stakeholders.

• Proven experience as a Data Scientist with exposure to real-world data science projects.
• Solid understanding of machine learning techniques and statistical modelling.
• Proficiency in Python and relevant libraries such as scikit-learn, TensorFlow, or PyTorch.
• Strong SQL skills and familiarity with data wrangling, feature engineering, and model deployment.
• Experience working with cloud platforms, preferably Microsoft Azure.
• Excellent problem-solving skills with the ability to translate complex data into clear insights.

Job Description
We are seeking a skilled AI Practitioner/Data Scientist with a deep specialization in Generative AI and a robust background in data. The ideal candidate will have hands-on experience in designing, developing, and deploying AI models with a focus on generative techniques. This role demands a strong understanding of data engineering principles, making the candidate well-equipped to handle the data pipeline challenges inherent in AI and data projects.

GenAI Model Development

• Design, implement, and optimize operational and analytical solutions utilizing Generative AI models.
• Develop and fine-tune RAG (Retrieval Augmented Generation) frameworks utilizing LLM frameworks such as LangChain, Llama-Index or other generative frameworks.
• Experiment with new architectures and techniques to push the boundaries of generative AI.

Data Engineering

• Collaborate with data engineering/analyst teams to ensure the availability and quality of data needed for model training and validation.
• Design and maintain scalable data pipelines to handle large volumes of structured and unstructured data.
• Perform ETL tasks with to create optimal data-warehousing structures (Dimensional modelling) for optimal storage, ease of use and maintainability.
• Integrate data from various sources and ensure its proper storage, transformation, and accessibility.

Deployment And Scaling

• Deploy AI and generative AI models in production environments, ensuring they meet performance, scalability, and reliability requirements.
• Work with DevOps teams to automate the deployment and monitoring of AI models.
• Optimize model inference for performance and cost efficiency in cloud and on-premises environments.

Collaboration And Communication

• Collaborate with cross-functional teams, including data scientists, engineers, product managers, and business stakeholders, to align AI solutions with business objectives.
• Communicate complex AI concepts and findings to non-technical stakeholders.

Continuous Learning And Innovation

• Stay up to date with the latest advancements in Generative AI and data engineering.
• Participate in conferences, workshops, and other professional development opportunities.
• Contribute to research publications and patent filings in the field of Generative AI.

Requirements

• 3+ years of experience in AI, with a focus on Generative AI (1-2 years).
• Proven experience in data engineering, including data pipeline development, ETL processes, and database management.
• Hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and Generative AI frameworks.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).

Technical Skills

• Proficiency in programming languages such as Python, SQL, and familiarity with data manipulation libraries (e.g., Pandas, NumPy).
• Strong knowledge of machine learning algorithms, neural networks, and generative models.
• Experience with data storage solutions (e.g., Hadoop, Spark, Vector databases).
• Knowledge of MLOps practices, including model versioning, monitoring, and retraining

Soft Skills

• A proactive attitude with a passion for learning and innovation.
• Excellent communication and teamwork abilities.
• Strong problem-solving and analytical skills.

Preferred Qualifications And Certification

• Bachelor's or master's degree in Computer Science, Data Science, Machine Learning, or a related field.

• Master's or Ph.D. is advantageous

• Any of the following certifications are highly advised:

• AWS Certified AI Practitioner

• AWS Certified Machine Learning
• GCP Professional Machine Learning Engineer
• GCP Associate Cloud Engineer
• Microsoft Azure AI engineer
• Microsoft Azure Data Science.

*Job Details
Take note that applications will not be accepted on the below date and onwards, kindly submit applications ahead of the closing date indicated below. *
31/10/25

All appointments will be made in line with FirstRand Group's Employment Equity plan. The Bank supports the recruitment and advancement of individuals with disabilities. In order for us to fulfill this purpose, candidates can disclose their disability information on a voluntary basis. The Bank will keep this information confidential unless we are required by law to disclose this information to other parties.