Specialist Data Scientist

Role purpose
Assisting in the development and implementation of machine learning solutions to support business objectives, enhance operational efficiency, and improve customer experiences.

Developing predictive models with large and varied datasets and working collaboratively with a team of professionals across Advanced Machine Learning, technology, data, and customer functions.

Contributing to the growth and advancement of the Advanced Machine Learning capability across Vodafone globally.

Your Responsibilities Will Include
Programming Languages: Proficiency in programming languages such as Python (preferred) or R is essential for data analysis, data manipulation, and building machine learning models.

Statistical Knowledge: Understanding of fundamental statistical concepts and methods is crucial for exploratory data analysis, hypothesis testing, and model evaluation.

Data Manipulation and Cleaning: Ability to preprocess and clean data, handle missing values, and ensure data quality is essential for accurate and reliable analysis. Using Big Data Tools to extract and process data , Pyspark , Ray, Dask, Hive.

Data Visualization: Skills in data visualization libraries like Matplotlib, Seaborn, or ggplot2 to create informative and visually appealing plots and charts for better communication of insights.

Machine Learning: Basic knowledge of machine learning algorithms, including supervised and unsupervised learning, and the ability to apply them to real-world datasets. Experience in production use cases. CRISDM-Process Modelling.

Data Analysis Libraries: Familiarity with data manipulation libraries such as Pandas and data processing frameworks like NumPy. Exposure Analysis in Python and using Pyspark Dataframes

SQL: Proficiency in SQL for querying and managing relational databases to extract and process data efficiently. Tradition Databases and Big Data Data Bases (ATHENA/HIVE/PRESTO/).

Data Storytelling: The ability to communicate results effectively, both verbally and visually, to non-technical stakeholders is crucial for a data scientist to make an impact.

Problem-Solving: Strong problem-solving skills to identify the right questions to ask, define the analytical approach, and interpret the results accurately.

Version Control: Familiarity with version control systems like Git to collaborate with team members effectively and track changes in code.

Big Data Technologies: Exposure to big data tools and frameworks like Apache Spark or Hadoop is beneficial for working with large-scale datasets. (AWS Cloud Tools Advantages)

Database Knowledge: Understanding of various types of databases, including relational databases, NoSQL databases, and data warehousing concepts.Mathematics: A basic understanding of linear algebra, calculus, and probability theory is valuable for comprehending and developing machine learning models.

Domain Knowledge: Familiarity with the specific industry or domain in which the data scientist operates, enabling them to apply data-driven solutions to domain-specific problems. Experience business applications for ML.

Continuous Learning: A willingness to stay updated with the latest trends, tools, and techniques in the field of data science through self-learning, courses, or workshops.

Collaboration: Ability to work effectively in a team, communicate ideas, and share knowledge with colleagues.

Time Management: Effective time management skills to prioritize tasks and meet project deadlines efficiently

The ideal candidate for this role will have
Technical / Professional Qualifications:
2-4 years of experience in a similar data science role.

Bachelor's or Master's Degree in quantitative fields such as Mathematics, Statistics, Economics, Computer Science, Engineering, Artificial Intelligence, or related disciplines.

Proficiency in data manipulation, including structured data tools (e.g., SQL) and unstructured data tools and platforms (e.g., Hadoop, Spark, NoSQL).

Familiarity with at least one programming language, such as Python/ Pyspark.

Knowledge of machine learning libraries (e.g., scikit-learn, TensorFlow, Pytorch, H20) and fundamental techniques (e.g., clustering, regression, time-series analysis).

Experience with visualization tools like Tableau or Qlik for data exploration and presentation.

Strong interest in staying updated with the latest advancements and emerging technologies in Machine Learning.

Analytical and expansive thinking with a strong desire to deliver and develop.

Good interpersonal communication and presentation skills

Ability to work in a fast-paced environment

Core competencies, knowledge, and experience:
Assisting in the development of machine learning models for various business areas using the big data platform.

Writing prototype code, such as PySpark, for automating the training and scoring of machine learning models.

Tracking and reporting the performance of machine learning models using tools like Qlik.

Utilizing data visualization techniques to effectively engage stakeholders and communicate insights.

Collaborating with senior data scientists to deliver project milestones to meet business requirements.

Working closely with the Big Data Engineering team to support data ingestion for various use cases.

Collaborating with the Big Data Production Data Engineering team to automate and deploy models into production.

Closing date for Applications: 16 October 2023
The base location for this role is, Vodacom Campus, Midrand.
The Company s approved Employment Equity Plan and Targets will be considered as part of the recruitment process. As an Equal Opportunities employer, we actively encourage and welcome people with various disabilities to apply.

Vodacom is committed to an organisational culture that recognises, appreciates and values diversity & inclusion.