Key Accountabilities

• Collect, acquire, and integrate data from various sources, such as databases, APIs, and external datasets.
• Ensure data quality and cleanliness through data cleaning and pre-processing.
• Conduct EDA to understand the characteristics of the data, identify patterns, and detect outliers.
• Visualize data using appropriate tools to gain insights.
• Engineer relevant features from raw data to enhance the performance of machine learning models.
• Select and transform variables for predictive modelling.
• Develop and train machine learning models for tasks like classification, regression, clustering, and recommendation.
• Optimize models for accuracy and efficiency.
• Create meaningful and visually appealing data visualizations to communicate findings to stakeholders.
• Apply statistical methods to analyse data and test hypotheses.
• Use statistical tests for significance and confidence interval estimation.
• Deploy machine learning models in production environments.
• Ensure models are scalable, maintainable, and continually monitored for performance.
• Collaborate with cross-functional teams, including data engineers, domain experts, and business analysts, to understand requirements and objectives.
• Develop a deep understanding of the organization’s business goals and challenges.
• Align data science projects with business objectives.
• Adhere to ethical data practices, ensuring data privacy, security, and compliance with regulations.
• Maintain thorough documentation of data analysis, methodologies, and model development for reproducibility and knowledge sharing.
• Stay updated on the latest advancements in data science, machine learning, and relevant technologies.
• Manage data science projects, including scoping, timelines, and resource allocation.
• Prioritize tasks and ensure projects are delivered on time and within budget.
• Establish a feedback loop with stakeholders to incorporate their insights and refine models or analysis.
• Contribute to the development of a data strategy that aligns with the organization’s long-term goals.
• Deliver clear and concise presentations and reports to communicate findings, insights, and recommendations to executives and decision-makers.
• Ensure that machine learning models are interpretable, explaining the rationale behind predictions.
• Implement data governance policies and practices to maintain data quality, integrity, and accessibility.
• Formulate and test hypotheses to answer specific business questions using data

Qualifications

Bachelor’s Degree – Information Technology, Experience in a similar environment