Model à United States

This is an excellent opportunity for a SENIOR DATA SCIENTIST / MACHINE LEARNING ENGINEER to own End-to-End Projects in a BIG 4 CONSULTANCY FIRM.

Based in either CAPE TOWN or JOHANNESBURG, this SENIOR DATA SCIENTIST / MACHINE LEARNING ENGINEER role offers a salary of R1.4m – R1.8m

THE COMPANY:

This is one of the BIG 4 FIRMS.

You will join a cutting-edge emerging technology team, focused on delivering advanced, scalable, and impactful analytics solutions across Africa. They blend Data Science, Machine Learning, Automation, and Quantum Computing to solve complex business challenges in novel and commercially viable ways.

This is a growing team of data-native, solutions-driven, cloud-savvy technologists - and are looking for someone who’s as comfortable building a neural net as they are designing a resilient data pipeline.

THE ROLE:

As a SENIOR DATA SCIENTIST / MACHINE LEARNING ENGINEER, you will own and drive the end-to-end lifecycle of AI/ML solutions — from data ingestion to production deployment. This role combines advanced data science with robust data engineering expertise, enabling scalable, high-performance, real-world implementations.

You will work on high-impact client projects across industries, and collaborate with cross-functional teams of engineers, consultants, and domain experts to deliver AI-driven solutions that create tangible value.

Key Responsibilities:

Lead full lifecycle projects, including:

• Data acquisition, wrangling, and cleaning
• Exploratory data analysis & feature engineering
• Model development, training, validation, and optimisation
• Production deployment, monitoring and maintenance
• Design and build scalable, automated data pipelines (ETL/ELT) using tools such as Apache Airflow, Spark, or dbt
• Work with structured and unstructured data from various sources including APIs, flat files, and relational databases
• Design, optimise, and manage cloud-based data lakes/warehouses (e.g., Snowflake, BigQuery, Redshift, Azure Data Lake)
• Optimise data storage, transformation, and retrieval workflows for performance and cost-efficiency
• Collaborate with DevOps/Engineering teams to build robust CI/CD pipelines for model and data deployment
• Communicate findings and insights to both technical and non-technical stakeholders
• Contribute to internal capability development, mentorship, and thought leadership

REQUIRED SKILLS & EXPERIENCE:

Data Science & ML:

• Proven experience in applying supervised, unsupervised, and deep learning models in real-world projects
• Experience with model evaluation, interpretability, and MLOps best practices
• Strong Python skills and ML libraries: pandas, scikit-learn, TensorFlow, PyTorch, XGBoost, etc.

Data Engineering:

• Strong SQL and data modeling skills
• Experience designing and implementing scalable data pipelines using Airflow, Luigi, Spark, Databricks, or similar
• Solid experience in ETL/ELT, data transformation and orchestration tools
• Proficiency in working with cloud platforms (Azure, AWS, GCP)
• Familiarity with containerisation (Docker) and version control (Git)

Bonus:

• Experience with streaming data architectures (Kafka, Kinesis)
• Familiarity with DataOps or MLOps tooling
• Previous consulting or client-facing experience
• Exposure to financial services, retail, public sector or telecoms

QUALIFICATIONS:

• Degree in Computer Science, Engineering, Statistics, Applied Mathematics or equivalent
• Postgraduate qualification (MSc/PhD) is a plus
• 5+ years of relevant experience in Data Science and/or Data Engineering roles