Data Analytics / Data Science / Machine Learning

Data Analysis, Visualization and Reporting: - Creating comprehensive data visualizations and reports. - Utilizing tools like PowerBI, Google Data Studio/Looker, QlikView, Tableau, and Google Sheets. - Generating actionable insights to inform business decisions. - Employing data mining techniques to optimize business operations. Machine Learning Solutions: - Developing and deploying machine learning models. - Building predictive analytics to drive business strategies. Data Engineering: - Designing and managing data pipelines. - Utilizing platforms such as Google Cloud Platform, MySQL, PostgreSQL, Google BigQuery, and MS SQL Server. Technical Expertise: - Proficiency in tools and environments including Git, Kubernetes, Docker, and various SQL databases, Python, R, MS Excel.

As a passionate and self-driven Data Scientist or Data Analyst I have 5+ years of experience working in Data Science, Data Engineering and Data Analytics roles within the e-commerce or retail, delivery optimisation, telecommunications and media and entertainment spaces. I’ve worked extensively with developing and deploying machine learning solutions, data visualisation or reporting, building actionable insights for the business and data mining techniques to drive data-driven strategies in business units such as digital marketing, supply chain, promotions, planning, customer operations with the aim to drive and optimise business profitability, productivity, efficiency and enhance customer experience. I work with a variety of tools and environments including: Google Cloud Platform, Git, Kubernetes, Docker, Python, R, PowerBI, Google Data Studio/Looker, MySQL, PostgreSQL, Google Big Query, MS SQL Server, MS Excel, QlikView, Tableau, Google Sheets and Nexidia Analytics.

Project Scope and Objectives: - Clear definition of the project goals and expected outcomes. - Detailed requirements and specifications for the project and contextual background. Access to Data: - Relevant datasets in an accessible format, where applicable. - Information on data sources and any necessary permissions for data access, where applicable. Timelines: - Project timelines, including any critical deadlines.

Initial Consultation and Planning: - Discuss project scope, objectives, and requirements. - Define project plan with timelines and milestones. - Set up communication channels for regular updates. Data Collection and Preparation: - Gather necessary data from the client or external sources. - Clean and preprocess data for quality and consistency. - Perform exploratory data analysis to understand key patterns. - Model Development (for ML Projects): - Select appropriate machine learning algorithms. - Train, validate, and fine-tune the models. Data Analysis (for Data Analytics Projects): - Conduct in-depth data analysis to uncover insights. - Use statistical methods and data mining techniques. - Create visualizations and reports. Implementation and Deployment: - Integrate the solution into the client's systems. - Ensure scalability, reliability, and maintainability. - Provide documentation and training. Evaluation and Feedback: - Monitor solution performance. - Gather client feedback for improvements. - Make necessary adjustments. Ongoing Support and Maintenance: - Offer post-deployment support. - Provide regular updates and maintenance. - Collaborate for continuous improvement and additional projects.