Data Scientist – Fintech Job opening in Cape Town Nov 2016

By | November 23, 2016

Data Scientist – Fintech Job opening in Cape Town Nov 2016

Job vacancy : Data Scientist – Fintech Work in South Africa 2016

Employment Position : Recruit Digital Latest Jobs 2016


Remuneration: R30000 – R60000 per month Basic salary
Benefits: Equity
Location: Cape Town
Type: Permanent Permanent
Reference: #815


Be a part of delivering socially responsible financial services to the masses; make it possible for students from more than 100 countries to obtain the finance to fulfil their dream of studying at the world’s top universities and schools. You will help to build a product which makes a very real difference in the world.

We are a small non-hierarchical team; this means that you are going to get exposure to all aspects of our business immediately. You’ll gain as much accountability as you can handle as scale the company.

Our goals is to build one of the top FinTech teams in the world. This means putting a lot of time into ensuring we only hire people with exceptional potential, and creating the best working environment possible. If you want to work somewhere where you’re learning from some of the best brains in Fintech, this is it.


  • Work at a successful global growth company.
  • Experience the excitement and learn from being part of an incredibly fast growing young company. No kidding – 4x growth. Happening right now!
  • Help to build a platform that helps to make a very real difference in the world.
  • Enjoy the agility and flexibility offered by a startup culture. We wear shorts to work.
  • Receive a competitive salary, and share options in the company. Since you will be playing a key role in making us successful, it is only right that you have a stake in the rewards of our success.
  • Enjoy healthy snacks and team lunches and weekly yoga sessions.

Your role

  • Collaborate with talented Product Owners, Software Engineers and Functional Experts to improve the impact of our products, systems and processes using data derived from the teams data science and machine learning activities.
  • Look for relevant sources of data (internally and externally) to enrich data input to analysis and machine learning pipelines.
  • Build automated measurement, learning and analysis pipelines for business products, systems and processes.
  • Build automated visualizations to communicate our data in an easy to consume format.
  • Educate techies about the methods used by the data science team so that they can contribute to data-based learning and integrate within the business.

What you need to be great at

  • Analytical thinking capability; be logical, systematic, strategic and pragmatic.
  • Technical competence; love coding and look to continuously improve and find better ways of doing things.
  • Strong attention to detail, both quantitative and qualitative, can organize large amounts of data from disparate sources.
  • Ability to explain complex concepts to others with patience and humility.
  • Excellent critical judgment; able to make good decisions, be trusted, respected and dependable, be proactive and responsive, ask the right questions, raise flags at the right time.

Track record

  • Experience with one or more classic data science & machine learning algorithms and techniques (NLP, text mining, linear regression, classification, clustering, topic modelling, support vector machines)
  • A strong background in software development with Python and relevant Python libraries (e.g. Numpy, SciPy, scikit-learn, Pandas).
  • A tertiary education that includes statistical methods and machine learning.
  • Experience working with relational databases.
  • Experience with using and deploying “big data” processing tools (Storm, Spark, Hadoop, MapReduce, Riak).

Nice to have’s (but we’ll trade off if everything else fits) 

  • Ideally you will have an MSc or PhD that incorporated statistical methods or machine learning.
  • A publication or publications on a machine learning topic in a reputable journal.
  • Experience with recurrent neural networks or deep learning.
  • Experience with d3 or other visualization tools.
  • Experience with other programming languages, especially Ruby and Javascript.
  • Experience with Bayesian statistics.
  • Start-up experience.