The role involves fetching information from various sources and analyzing it to get a clear understanding of how an organization performs, using statistical and analytical methods plus AI tools to automate specific processes within the organization and develop smart solutions to business challenges. The objective is to help the organization analyze trends to make better decisions. The role leverages data-driven insights to inform and guide decision-making within an organization. It plays a crucial role in extracting valuable knowledge from large and complex datasets, which can be used to solve specific business problems and drive innovation.
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
