Data Scientist / Data Engineer / Data Analyst

Role Description

We are seeking a highly analytical and technically capable data professional to transform complex data into meaningful insights, reliable data solutions, and actionable business recommendations. The role combines data analysis, data engineering, statistical modeling, and data-driven problem solving to support operational efficiency, strategic planning, and informed decision-making.

The successful candidate will work with structured and unstructured data, develop analytical solutions, maintain data pipelines, and collaborate with technical and business teams. Responsibilities may include data collection, transformation, visualization, statistical analysis, predictive modeling, database management, and the development of scalable data infrastructure.

Key responsibilities include:

• Collect, clean, transform, validate, and analyze data from multiple internal and external sources.
• Design and maintain reliable data pipelines, workflows, and data integration processes.
• Develop data models, analytical datasets, dashboards, reports, and visualization solutions.
• Analyze large and complex datasets to identify trends, patterns, opportunities, and potential risks.
• Apply statistical methods, machine learning techniques, and predictive models where appropriate.
• Develop and maintain scalable data architectures, databases, and data processing environments.
• Work with stakeholders to understand business requirements and translate them into data-driven solutions.
• Create meaningful dashboards and reports to communicate key performance indicators and analytical findings.
• Monitor data quality, accuracy, consistency, availability, and integrity across relevant systems.
• Optimize queries, data workflows, processing performance, and analytical processes.
• Support forecasting, experimentation, segmentation, and other advanced analytical initiatives.
• Collaborate with software engineers, product teams, business stakeholders, and other data professionals.
• Document data sources, models, pipelines, analytical methodologies, and technical processes.
• Identify opportunities to automate repetitive data tasks and improve data accessibility.
• Communicate complex analytical results in a clear and understandable manner to technical and non-technical audiences.

Qualifications

• Bachelor’s degree or equivalent qualification in Data Science, Computer Science, Statistics, Mathematics, Engineering, Information Technology, or a related field.
• Strong understanding of data analysis, statistical concepts, data structures, databases, and data management principles.
• Proficiency in SQL and familiarity with programming languages such as Python, R, or similar.
• Knowledge of data visualization and business intelligence tools such as Power BI, Tableau, Looker, or equivalent platforms.
• Familiarity with data engineering concepts, ETL/ELT processes, data pipelines, APIs, and data integration.
• Understanding of relational and non-relational databases, data warehouses, and modern data platforms.
• Familiarity with cloud-based data services and platforms such as AWS, Microsoft Azure, or Google Cloud is an advantage.
• Knowledge of machine learning, predictive analytics, or statistical modeling is an advantage.
• Strong analytical, quantitative, critical-thinking, and problem-solving skills.
• Excellent attention to detail and commitment to data accuracy and quality.
• Ability to organize, interpret, and communicate complex datasets effectively.
• Strong written and verbal communication skills with the ability to present insights clearly.
• Ability to work collaboratively with technical teams and business stakeholders.
• Strong organizational skills and ability to manage multiple data-related priorities.
• Familiarity with Git, version control, automation, or modern data development practices is an advantage.
• Relevant certifications in data science, analytics, cloud technologies, or business intelligence are an advantage but not mandatory.