SENIOR DATA SCIENTIST | MACHINE LEARNING & PRODUCTION ML – join leading FINTECH - CAPE TOWN (Hybrid), R1.2m – R1.5m

This is an excellent opportunity for a SENIOR DATA SCIENTIST who doesn’t merely build good models, but also has the engineering ability and inclination to take that model into production, keep it performing and demonstrate its commercial value.

Based in CAPE TOWN (Hybrid) this SENIOR DATA SCIENTIST role offers a salary of R1.2m – R1.5m.

THE COMPANY:

A well-established and growing South African FinTech business using technology and data to solve the biggest challenges facing SMEs: access to fast, intelligent and frictionless financial services.

They are not a traditional financial-services organisation trying to retrofit technology around an old operating model. Data, software and intelligent decision-making are central to the business.

They have built a sophisticated digital platform and are continuing to invest heavily in their Data capability as the business scales. For a strong Data Scientist, this creates an unusually interesting environment: the work is commercially important, the datasets are real, the problems are complex and the models you build have a direct impact on business decisions.

THE ROLE:

This position is ideal for a SENIOR DATA SCIENTIST wanting to take considerable ownership of machine learning capability. It’s an opportunity to work on genuine commercial problems, where the quality of your models and the decisions they enable have a direct impact on the business and its customers.

You'll be working closely with the Head of Data Engineering and a team of data and analytics engineers, with significant scope to influence how machine learning is developed, deployed and managed across the business.

The work spans the full lifecycle, from understanding the business problem and exploring the data through to developing, deploying, monitoring and improving models in production.

You'll be working on challenges involving predictive modelling, classification, segmentation, forecasting and experimentation, with applications across credit, product, operations and other business functions.

Importantly, this isn't a role where you'll spend your time building models that never make it beyond a presentation or a notebook. The expectation is that your work gets used. And that you take responsibility for what happens after it goes live.

REQUIRED SKILLS:

You will need to have a strong grounding in DATA SCIENCE and MACHINE LEARNING, and be equally comfortable with the engineering disciplines required to make ML work reliably in production.

Around 3-5 years' experience in data science, machine learning or advanced analytics.

Proven hands-on experience developing and deploying ML models into production.

Experience monitoring live models, identifying performance degradation or drift, and making the necessary improvements.

At least 3 years' commercial experience with Python and SQL.

Strong software engineering practices, including version control, testing and code reviews.

The ability to work with business stakeholders, understand commercial problems and turn them into effective analytical solutions.

A relevant quantitative or technical degree.

Experience with Azure, Snowflake, dbt, MLflow, CI/CD, Docker or workflow orchestration would be advantageous.

Experience in financial services, lending or FinTech would also be particularly relevant.

(One important point about your background: I’m not exclusively looking for someone with the job title Data Scientist. If you've come through a software engineering background, moved into machine learning and have since developed strong hands-on experience building and deploying models, I’d be equally interested in speaking to you. What matters is your ability to combine sound modelling techniques with the engineering skills to make those models genuinely useful).

WHY CONSIDER IT?

Because you'll have the opportunity to do more than develop models against a predefined list of requirements.

You'll have real ownership, considerable technical influence and the opportunity to help shape how data science and machine learning are applied across a growing business.

You'll also work close to the commercial problems you're solving, rather than being several layers removed from the people using your work.

And you'll join a business where technology and data aren't peripheral functions. They're fundamental to what the company does.

If you're a Data Scientist or ML Engineer who enjoys the engineering challenge as much as the modelling, this is worth a conversation.