Intermediate Data Scientist — ML & Big Data in Production

ATS Client

Johannesburg

Hybrid

ZAR 720,000 - 880,000

Full time

14 days+

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Job summary

ATS Client in Johannesburg seeks an analytical Intermediate Data Scientist to transform complex data into insights guiding strategic decisions. You will build and deploy ML models, work with structured and unstructured data, and collaborate with stakeholders to translate requirements into data-driven solutions.

The role requires hands-on experience with Hadoop, Spark, Kafka, and ML Ops, plus proficiency in Python, R, and SAS.

Qualifications

Honours Degree in Statistics, Computer Science, Data Science, Mathematics, or equivalent.
Strong analytical skills with a track record of translating data into actionable insights.
Proficient in Python, R, and SAS.
Hands-on experience with Spark, Hadoop, Kafka, and real-time data streaming.
Experience with ML Ops and deploying AI-based solutions end-to-end.
Ability to work independently and proactively identify data opportunities.
Strong communication and presentation skills.
Familiarity with Confluence or similar collaboration/documentation tools.
Experience with dashboards and visualization tools (R Shiny, Dash, Power BI).
Demonstrated evidence of deployed AI/ML solutions in production.

Responsibilities

Analyse large volumes of structured and unstructured data to uncover trends, patterns, and actionable insights that support business decision‑making.
Design, develop, and implement machine learning and statistical models to solve complex business problems.
Work closely with business stakeholders to understand requirements and translate them into data‑driven solutions.
Identify new opportunities within available datasets to improve operational efficiency, risk management, and customer insights.
Develop data pipelines and workflows to support scalable data science and machine learning solutions.
Deploy and monitor machine learning models in production environments, ensuring performance, reliability, and continuous improvement.
Implement and monitor Responsible AI and ML Ops solutions for model governance.
Utilize big data technologies such as Hadoop, Spark (Python and R), I‑Spark, and Kafka for processing and streaming large datasets.
Work with cloud platforms, preferably AWS, for data storage, processing, and analytics.
Collaborate with cross‑functional teams and communicate findings effectively to technical and non‑technical stakeholders.