Data Scientist - Machine Learning

Placements24; Randburg; Hybrid; ZAR 600,000 - 900,000; Full time

Benefits offered by this job

Competitive salary and bonus structure

Hybrid work model

Comprehensive health and wellness

Job summary

Placements24 is seeking a skilled Data Scientist with ML expertise to join their analytics team in Randburg. Hybrid work enables flexible remote collaboration with in-office teamwork to drive data-driven decisions.

The role focuses on building predictive models, extracting actionable insights, and communicating complex results to diverse stakeholders. Strong Python/R skills and ML libraries are essential.

Qualifications

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• 3+ years of professional experience as a Data Scientist or Machine Learning Engineer.
• Strong programming skills in Python or R, with expertise in libraries like Scikit-learn, TensorFlow, or PyTorch.
• Solid understanding of statistical modeling, machine learning algorithms, and data mining techniques.
• Experience with data manipulation and analysis tools (e.g., Pandas, SQL).
• Excellent problem-solving abilities and strong communication skills.

Responsibilities

• Develop, train, and deploy machine learning models for predictive analytics, classification, regression, and clustering tasks.
• Analyze large, complex datasets to identify trends, patterns, and insights using statistical methods and data mining techniques.
• Collaborate with business stakeholders to understand their needs and translate them into data science projects.
• Design and conduct A/B tests and experiments to evaluate the performance of models and product features.
• Communicate complex findings and recommendations clearly and concisely to both technical and non-technical audiences.
• Stay current with advancements in data science, machine learning, and artificial intelligence.

Skills

Communication skills

Problem solving

Python

R

Scikit-learn

TensorFlow

PyTorch

Pandas

SQL

Education

Master's or Ph.D. in CS/Statistics/Math or related field

Tools

Python

R

Scikit-learn

TensorFlow

PyTorch

Pandas

SQL

Job description

About the Role

Our client is seeking a skilled Data Scientist with expertise in Machine Learning to join their analytics team in Randburg. This role involves leveraging large datasets to build predictive models, extract actionable insights, and drive data-informed decision-making across the organization. You will work on challenging problems, applying advanced statistical and machine learning techniques. This hybrid position allows for flexible remote work combined with essential in-office collaboration to foster team synergy and project alignment.

Key Responsibilities

• Develop, train, and deploy machine learning models for predictive analytics, classification, regression, and clustering tasks.


• Analyze large, complex datasets to identify trends, patterns, and insights using statistical methods and data mining techniques.


• Collaborate with business stakeholders to understand their needs and translate them into data science projects.


• Design and conduct A/B tests and experiments to evaluate the performance of models and product features.


• Communicate complex findings and recommendations clearly and concisely to both technical and non-technical audiences.


• Stay current with advancements in data science, machine learning, and artificial intelligence.

Requirements

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.


• 3+ years of professional experience as a Data Scientist or Machine Learning Engineer.


• Strong programming skills in Python or R, with expertise in libraries like Scikit-learn, TensorFlow, or PyTorch.


• Solid understanding of statistical modeling, machine learning algorithms, and data mining techniques.


• Experience with data manipulation and analysis tools (e.g., Pandas, SQL).


• Excellent problem-solving abilities and strong communication skills.

Benefits

• Competitive salary and bonus structure.


• Hybrid work model offering work-life balance.


• Comprehensive health and wellness benefits.


• Access to cutting-edge tools and technologies.


• Opportunities for continuous learning and professional growth through conferences and workshops.