Data Analyst

A Bachelor's degree in Computer Science, Information Technology, Data Science, Statistics, Mathematics, Engineering, or a related discipline is preferred. Candidates with strong project-based experience and a demonstrated interest in AI and analytics are encouraged to apply.

• →Collect, clean, transform, and analyze structured and unstructured datasets to identify meaningful trends and business insights.
• →Apply statistical and Machine Learning techniques to support forecasting, classification, segmentation, and other analytical use cases.
• →Assist in developing, testing, and improving machine learning models based on business requirements.
• →Work with stakeholders to understand business challenges and identify opportunities where Artificial Intelligence for Business can improve processes, decision-making, customer experience, or operational efficiency.
• →Support the development and integration of data-driven applications using .NET technologies.
• →Collaborate with .NET developers, data scientists, business teams, and other stakeholders to deliver analytical solutions.
• →Perform exploratory data analysis and create reports, dashboards, and visualizations that communicate findings clearly.
• →Validate data quality, identify inconsistencies, and ensure analytical outputs are accurate and reliable.
• →Assist in deploying, monitoring, and maintaining analytical and AI solutions.
• →Document data processes, analytical methodologies, models, and business requirements.
• →Stay updated with emerging developments in Machine Learning, Artificial Intelligence, data analytics, and business technology.

• Strong foundation in Data Analysis and Machine Learning concepts.
• Understanding of Artificial Intelligence for Business and practical AI use cases.
• Knowledge of .NET development, including familiarity with C# and the .NET framework/ecosystem.
• Ability to work with datasets, perform data cleaning, transformation, and exploratory analysis.
• Basic understanding of statistics, predictive modeling, and machine learning algorithms.
• Strong analytical and problem-solving abilities.
• Ability to understand business requirements and translate them into technical or analytical solutions.
• Good communication and collaboration skills.

• JavaScript for developing or supporting data-driven applications and visualizations.
• Python for data analysis, machine learning, automation, and working with libraries such as Pandas or NumPy.
• R for statistical analysis, data exploration, and modeling.
• Familiarity with SQL, databases, APIs, data visualization tools, or cloud-based analytics platforms.
• Academic projects, internships, certifications, or personal projects involving AI, Machine Learning, Data Analytics, or .NET development.