Data Scientist at Oando

Reports To:

Assistant Manager, Solution Architect

Responsible For:

• Developing predictive models and machine learning algorithms.
• Analyzing large datasets to derive actionable insights.
• Collaborating with business units to support data-driven decisions.
• Automating processes to improve operational efficiency and decision-making.

Overall, Purpose of Job:

The Data Scientist is responsible for developing advanced analytics models and machine learning algorithms that support key business functions. The role will analyze large, complex datasets across all subsidiaries, delivering insights to drive strategy and decision-making across the organization.

Key Responsibilities:

Predictive Modeling & Advanced Analytics:

• Develop and deploy machine learning models to solve business problems, such as predictive maintenance for oil and gas equipment.
• Design and execute experiments to test hypotheses and improve model accuracy.
• Leverage statistical techniques and machine learning frameworks to analyze large datasets and discover trends and patterns.
• Continuously improve model performance and scalability.

Data Analysis and Visualization:

• Analyze structured and unstructured data to generate actionable insights for different business units.
• Create compelling visualizations and dashboards using BI tools (e.g., PowerBI, Tableau) to present complex results to non-technical stakeholders.
• Identify key trends and metrics, helping to drive strategy across all business subsidiaries.

Automation and Operational Efficiency:

• Automate data collection, processing, and reporting to improve efficiency across departments.
• Collaborate with data engineers to build efficient data pipelines supporting the analytics needs of the business.
• Contribute to the automation of routine processes, reducing manual effort in operations.

Data Management and Governance:

• Adhere to data governance practices, ensuring data quality, security, and privacy in line with regulations.
• Contribute to the development of data standards and best practices for data storage, access, and usage.
• Monitor data accuracy and report inconsistencies for immediate action.

Collaboration:

• Work closely with IT Business Information Coordinators and other business stakeholders to identify opportunities for leveraging data.
• Collaborate with data engineers to ensure the availability and quality of data for analysis and modeling.
• Ensure machine learning initiatives are aligned with the overall IT strategy and infrastructure.

Key Performance Indicators (KPIs):

• Model Accuracy: Maintain at least 85% accuracy in predictive models deployed across key business units.
• Business Intelligence Utilization: Increase the adoption of BI tools by 20% across departments within the first year.
• Automation Impact: Achieve a 15% reduction in manual reporting efforts through automation.
• Data Quality Compliance: Ensure 95% compliance with internal data accuracy and governance standards.

Person Specification:

• Bachelor’s degree in data science, statistics, mathematics, or a related field.
• 3-5 years of experience in data science, with a focus on predictive modeling and machine learning.
• Proficiency in Python, R, and SQL for data analysis and model development.
• Experience with machine learning frameworks such as TensorFlow or PyTorch
• Strong skills in statistical analysis and data visualization using tools like PowerBI, Tableau, or Matplotlib.

Required Competencies:

• Strong problem-solving skills and a deep understanding of data-driven approaches.
• Excellent communication and presentation skills, with the ability to translate complex findings into actionable insights.
• Experience in the energy sector or working with large-scale IoT dataset is a plus.
• Knowledge of cloud platforms (AWS, GCP, Azure) and big data technologies.
• Ability to work independently and collaboratively within a fast-paced environment.