Data Analyst and Modelling

Passion for applying AI and data-driven thinking to improve processes, enhance decision-making, and drive innovation.

As a Data Analyst and Modelling professional, you will be responsible for collecting, processing, and performing statistical analysis on large datasets to extract meaningful insights and build predictive frameworks that inform strategic business decisions.

Key Responsibilities

• Data Collection and Management: Gather data from primary and secondary sources (databases, APIs, surveys, spreadsheets), and design and maintain data systems and databases to ensure data accuracy, integrity, and accessibility.
• Data Cleaning and Preprocessing: Filter, clean, and reorganize raw, complex datasets to fix coding errors, handle missing values, and remove duplicates and outliers, ensuring data quality and consistency for analysis.
• Data Analysis and Interpretation: Use statistical techniques (like regression analysis and hypothesis testing) to interpret data, analyze results, identify trends, patterns, and correlations, and diagnose why certain outcomes occurred.
• Data Modelling: Create conceptual, logical, and physical data models to define how data elements are organized, stored, and interact, supporting effective analysis and predictive capabilities. This includes developing predictive models using statistical techniques and machine learning algorithms to forecast future outcomes.
• Reporting and Visualisation: Create and maintain reports, interactive dashboards, graphs, and other visualisations to effectively communicate complex findings and recommendations to non-technical stakeholders and leadership.
• Collaboration and Communication: Work closely with cross-functional teams (including programmers, engineers, and managers) to understand business needs, define KPIs, and translate technical findings into actionable business strategies and solutions.
• Process Improvement: Evaluate internal systems for efficiency, identify opportunities for process improvements, and recommend system modifications and data governance policies.

Required Skills & Qualifications

Technical Skills:

• Programming Languages: Proficiency in SQL (Structured Query Language) for database querying and management; Python for statistical analysis, automation, and machine learning.
• Data Visualization Tools: Experience with Excel for creating reports and dashboards.
• Statistical Software: Knowledge of statistical packages like Excel, SPSS, or SAS for advanced analysis.
• Database Management: Understanding of database design, development, and ETL (Extract, Transform, Load) frameworks.
• Machine Learning : Familiarity with building basic predictive models and algorithms.

Soft Skills:

• Strong analytical and problem-solving skills, with attention to detail.
• Excellent written and verbal communication skills, including data storytelling and presentation abilities.
• Critical thinking and a natural curiosity to explore data and uncover insights.
• Ability to work independently and collaboratively in a team-oriented, fast-paced environment.
• Strong organizational and project management skills.

Qualifications:

• A bachelor's degree in a relevant field such as Statistics, Mathematics, Computer Science, Economics, or Information Management is typically required.
• A master's degree in Data Science or Business Analytics can be beneficial for senior-level roles.
• Relevant professional certifications (e.g., Google Data Analytics Professional Certificate, Microsoft Certified: Power BI Data Analyst Associate) and a portfolio of real-world projects are highly valued.

Job Benefits & Perks:

As per General IT industry Practices