Remote Senior AI Data Scientist — Lead & Innovate ML

Placements24; Gqeberha; Hybrid; ZAR 1,500,000 - 2,400,000; Full time

Benefits offered by this job

Competitive salary

Performance bonuses

Remote health benefits

Retirement plan

Professional development budget

Flexible work hours

Work from anywhere

Job summary

Placements24 is seeking a Senior AI Data Scientist to join a distributed team in a fully remote role. You will lead complex ML projects and translate data insights into business value.

The ideal candidate has a Master's or PhD in a quantitative field with 6+ years in data science, strong Python or R skills, and experience with Pandas, NumPy, and ML frameworks. Mentoring juniors and collaborating with engineering to deploy models are key expectations.

Qualifications

• 6+ years of professional experience as a Data Scientist.
• Master's or PhD in a quantitative field.
• Expertise in ML, statistical modeling, and data mining.
• Proficiency in Python or R and ML libraries.
• Strong SQL and big data tech such as Spark or Hadoop.

Responsibilities

• Lead design, development, and deployment of advanced ML models.
• Analyze large datasets to identify trends and insights.
• Develop and validate predictive models for forecasting and segmentation.
• Mentor junior data scientists and collaborate with stakeholders.
• Collaborate with engineering to deploy models into production and monitor performance.

Skills

Python

R

Pandas

NumPy

Scikit-learn

TensorFlow

PyTorch

SQL

Spark

Hadoop

Statistical Modeling

Machine Learning

Education

Master's degree

PhD

Tools

Spark

Hadoop

Job description

Placements24 is seeking a Senior AI Data Scientist to join a distributed team in a fully remote role. You will lead complex ML projects and translate data insights into business value.

The ideal candidate has a Master's or PhD in a quantitative field with 6+ years in data science, strong Python or R skills, and experience with Pandas, NumPy, and ML frameworks. Mentoring juniors and collaborating with engineering to deploy models are key expectations.