Data Scientist

Some of the key responsibilities include:

1. Data mining involves extracting usable data from valuable sources.
2. Possesses extensive knowledge of Python, including a deep understanding of machine learning libraries.
3. Utilises machine learning tools to select features, create and optimise classifiers, and carry out preprocessing of both structured and unstructured data.
4. Proficient with deep learning models such as ANN, CNN, and NLP.
5. Enhances data collection procedures to encompass all relevant information for developing analytical systems.
6. Processes, cleanses, and validates the integrity of data for analysis.
7. Analyses large volumes of information to identify patterns and solutions.
8. Develops prediction systems and machine learning algorithms.
9. Proposes solutions and strategies to address business challenges.
10. Collaborates with business and IT teams.

Required Technical Skills:

1. Programming Skills Knowledge of statistical programming languages such as R and Python, along with database query languages like SQL, is desirable. 
2. Statistics Strong applied statistical skills, encompassing knowledge of statistical tests, distributions, regression, and maximum likelihood estimators, are essential for data-driven companies. 
3. Machine Learning A good understanding of machine learning methods is required. 
4. Strong Math Skills (Multivariable Calculus and Linear Algebra) - Understanding the fundamentals of Multivariable Calculus and Linear Algebra.
5. Data Wrangling – Proficiency in managing data imperfections is a key aspect.
6. Experience with Data Visualisation Tools such as Matplotlib, which assist in visually encoding data.