Senior Data Scientist, Predictive Analytics (Remote)

Placements24; Rustenburg; Hybrid; ZAR 1,200,000 - 1,800,000; Full time

Benefits offered by this job

Competitive salary

Health insurance

Remote work

Generous leave

Training & development

Retirement plan

Performance bonuses

Job summary

Placements24 is hiring a Senior Data Scientist with a strong background in Predictive Analytics to join our client’s team remotely. This role is focused on developing sophisticated predictive models to drive business decisions and enhance customer engagement.

You will own the end-to-end data science lifecycle—from data exploration and feature engineering to model building, validation, and deployment—while mentoring junior data scientists and collaborating with cross-functional teams.

Qualifications

• Master's or PhD in a quantitative field required.
• Five+ years as a Data Scientist focusing on predictive modeling.
• Strong programming with Python/R and ML libraries.

Responsibilities

• Design and implement advanced predictive models and ML algorithms.
• Analyze large datasets to identify trends and insights.
• Perform feature engineering and feature selection.
• Validate and deploy models to production.
• Communicate findings to technical and business stakeholders.
• Collaborate with cross-functional teams on data science problems.
• Develop dashboards to visualize model performance.
• Mentor junior data scientists and share knowledge.

Skills

Analytical thinking

Communication skills

Independent work

Team collaboration

Education

Master's degree in Statistics/Mathematics/CS

PhD in a quantitative field (preferred)

Tools

Python

R

scikit-learn

pandas

NumPy

TensorFlow

SQL

Tableau

Power BI

Matplotlib

Job description

Placements24 is hiring a Senior Data Scientist with a strong background in Predictive Analytics to join our client’s team remotely. This role is focused on developing sophisticated predictive models to drive business decisions and enhance customer engagement.

You will own the end-to-end data science lifecycle—from data exploration and feature engineering to model building, validation, and deployment—while mentoring junior data scientists and collaborating with cross-functional teams.