WHAT WE DO

These days there is a lot of talk about ‘artificial intelligence’, ‘machine learning’, ‘data science’ and the growing importance of ‘data’. This can all be a bit overwhelming.
We help our clients navigate through this space, and to improve their products, services and strategy through the thoughtful use of data science, machine learning, and behavioural insights.
By taking care of product development and model maintenance our approach allows companies to quickly scale their data science capacity and deliver a high return on investment with limited risk.
As co-creators with our clients, we share the same incentives – to deliver actionable and valuable insights which improve business efficiency and profitability.
Please take a look at our
product and services page to see what this actually means.

 

OUR APPROACH

We work with our clients to understand the business problem that needs solving, the information and data they have which can help, and how the solution could fit with their own processes.
We design a solution which might draw on one of our existing products or might require the development of a bespoke product. Given our extensive experience and suite of existing
products we can quickly build an effective solution.
We then maintain the solution or product to make sure that it continues to deliver value. If the client wants to build internal capacity we work together to transfer the ability to maintain and modify the product to the client’s team.

  • What data to collect?

  • How to extract data?

  • How to combine & structure for insight?

  • Using data for business decisions

  • Behavioural science

  • Financial & risk modelling

  • Computer science

  • The role of markets & prices

  • Productionise models

  • Meaningful visualisation

  • Timely reporting on results

  • Connect insights to strategy

  • Cross-sectional and panel statistics

  • Time series analysis

  • Machine learning approaches

    • Regressions

    • Trees and forests

    • Clustering

    • Support vector machine

  • Deep learning methods

    • Neural networks

 

TEAM

Rulof Burger

Rulof is an econometrician, game theorist, and behavioural and labour economist. He has a part-time associate professor position at the University of Stellenbosch, a doctoral degree from the University of Oxford, and master’s degrees from the University of Cambridge and the University of Stellenbosch.

Neil Rankin

Neil is focused on making the most effective use of data within an organisation and on building solutions and teams to make this possible. He has degrees from the University of Cape Town and Simon Fraser University, and a doctorate from the University of Oxford.

Richard Barry

Richard is engaged with the development of customer centric solutions and the use of the Internet of Things to capture data for further analysis. He has a computer science and electrical engineering background; and completed his graduate studies at the University of Stellenbosch. He is also responsible for new business developments and building client relationships.

Kevin Kotzé

Kevin is primarily interested in deep learning, the application of Bayesian methods, forecasting and the modelling of variables that evolve over time. He completed his graduate studies after receiving a doctorate from the University of Stellenbosch and has over twenty years of corporate and academic experience.

Cobus Burger

Cobus enjoys building and applying probabilistic models which have been applied in a variety of contexts. He has taught graduate econometric courses at the Universities of Cape Town and the Western Cape. He completed his graduate training at the University of Oxford and the University of Stellenbosch, and is in the process of completing a post doctoral fellowship.

Hendrik van Broekhuizen

Hendrik is interested in data management, data science workflows, and the visualisation of results. He completed his doctoral studies at the University of Stellenbosch where his research focussed on the linkages between education, skills development, and labour market outcomes.

Diana Pholo

Diana is interested in intelligent systems, natural language processing, chatbots,  and integrating machine learning models into web applications. She has nine year experience lecturing at the Tshwane University of Technology, where she completed her MTech in Intelligent Industrial Systems. She also holds an MSc in Electrical and Electronic Systems from the French-South African Institute of Technology.

Michael Hamman

Michael is involved across the data science pipeline from data extraction and ingestion, through exploratory analysis, building interactive data visualisations and maintaining machine learning models in production. He completed his master's degree in economics at the University of Stellenbosch.

Mahlatse Mashala

Mahlatse has a keen interest in machine learning models, reporting and data visualization, and how data science can be used to solve real business problems.  She holds a degree from the University of Cape Town, where her focus areas of study were in economics and statistics.  She is in the process of completing her BCom Honours degree in Financial Analysis and Portfolio Management at the University of Cape Town.

Dennis Irorere

Dennis has a knack for spatial data collection and analysis, and is particularly interested in how people and technology come together to create and use this type of data. His interests include geographical information system (GIS) tools, database design, and data science workflows. Dennis is one of the 2018 YouthMappers Research Fellow, a program funded by USAID, which is designed to enlist, enable, and showcase the contributions of open geospatial data for research on resilience of vulnerable populations around the world, and is very active in the AfricaR community.

 

CONTACT US

We want to hear from you!

Neil Rankin

The Brickall
Stratford-upon-Avon
CV37 8QL

United Kingdom

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MACHINE LEARNING PRODUCT DESIGN STUDIO