Data Scientist Specialist - Cape Town City Centre
Data Analysis: Conducting exploratory data analysis (EDA) to understand
the characteristics and structure of datasets, including data cleaning,
preprocessing, and transformation.
Statistical Modeling: Applying statistical methods and techniques to analyze
data, make predictions, and infer relationships between variables.
Machine Learning: Developing and deploying machine learning models to
solve predictive modeling, classification, clustering, and regression tasks.
Programming: Proficiency in programming languages such as Python, R, or
SQL for data manipulation, analysis, and modeling.
Data Visualization: Creating visualizations, such as charts, graphs, and
dashboards, to effectively communicate insights and findings to stakeholders.
Big Data Technologies: Familiarity with big data technologies and
frameworks, such as Hadoop, Spark, or distributed computing platforms, for
handling and processing large-scale datasets.
Domain Knowledge: Understanding of the specific industry or domain in
which they work, including relevant business processes, challenges, and
opportunities.
Experimental Design: Designing and conducting experiments to test
hypotheses, validate models, and optimize outcomes.
Data Storytelling: Ability to articulate findings and insights from data in a
clear, concise, and compelling manner to non-technical audiences.
Collaboration: Working collaboratively with cross-functional teams, including
data engineers, business analysts, and stakeholders, to define objectives,
gather requirements, and deliver solutions.
Continuous Learning: Keeping up-to-date with advancements in data science, machine learning algorithms, and related technologies through self study, research, and professional development activities. Apply Now
the characteristics and structure of datasets, including data cleaning,
preprocessing, and transformation.
Statistical Modeling: Applying statistical methods and techniques to analyze
data, make predictions, and infer relationships between variables.
Machine Learning: Developing and deploying machine learning models to
solve predictive modeling, classification, clustering, and regression tasks.
Programming: Proficiency in programming languages such as Python, R, or
SQL for data manipulation, analysis, and modeling.
Data Visualization: Creating visualizations, such as charts, graphs, and
dashboards, to effectively communicate insights and findings to stakeholders.
Big Data Technologies: Familiarity with big data technologies and
frameworks, such as Hadoop, Spark, or distributed computing platforms, for
handling and processing large-scale datasets.
Domain Knowledge: Understanding of the specific industry or domain in
which they work, including relevant business processes, challenges, and
opportunities.
Experimental Design: Designing and conducting experiments to test
hypotheses, validate models, and optimize outcomes.
Data Storytelling: Ability to articulate findings and insights from data in a
clear, concise, and compelling manner to non-technical audiences.
Collaboration: Working collaboratively with cross-functional teams, including
data engineers, business analysts, and stakeholders, to define objectives,
gather requirements, and deliver solutions.
Continuous Learning: Keeping up-to-date with advancements in data science, machine learning algorithms, and related technologies through self study, research, and professional development activities. Apply Now
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