What you’ll bring

All qualifications

Core experience

1 4–7 years of data science experience
2 Proficiency in SQL and Excel
3 Production engineering best practices: Git, Docker
4 Working knowledge of AWS Cloud
5 Foundational ML: regression, classification, optimisation techniques

Qualification wording

1 4–7 years of data science experience

2 Proficiency in SQL and Excel

3 Production engineering best practices: Git, Docker

4 Working knowledge of AWS Cloud

5 Foundational ML: regression, classification, optimisation techniques

Tools in this posting

SQL
AWS
Excel
Docker

Source

1 4–7 years of data science experience 2 Proficiency in SQL and Excel 3 Production engineering best practices: Git, Docker

3 Production engineering best practices: Git, Docker 4 Working knowledge of AWS Cloud 5 Foundational ML: regression, classification, optimisation techniques

2 Proficiency in SQL and Excel 3 Production engineering best practices: Git, Docker 4 Working knowledge of AWS Cloud

Job description

View original posting ↗

Data Scientist

Experience
4–7 years

Requirements

1
4–7 years of data science experience
2
Proficiency in SQL and Excel
3
Production engineering best practices: Git, Docker
4
Working knowledge of AWS Cloud
5
Foundational ML: regression, classification, optimisation techniques

Responsibilities

1
Analyse structured and unstructured data; build and deploy ML models
2
Collaborate cross-functionally for scalable, actionable insights
3
Design and execute A/B testing experiments
4
Develop subject matter expertise across diverse project domains
5
Translate analytical findings into clear recommendations for stakeholders
6
Work with data engineering to operationalise models and pipelines
7
Create visualisations to communicate data insights effectively
8
Stay updated on emerging data science tools and techniques