Data Analyst at Experian

Who are we?

Experian gathers, analyses and processes data in ways others can't. We help individuals take financial control and access financial services, businesses make smarter decisions, lenders lend more responsibly, and organizations prevent identity fraud and crime. Our 17,800 people in 45 countries believe the possibilities for you, and our world, are growing. We're investing in new technologies, talented people and innovation so we can help create a better tomorrow.

What do we have to offer?

Not only do we offer our employees a competitive benefits package and flexibility to have a good work life balance. We offer you an exciting, challenging environment where you get the opportunity to work in an international team and continuously learn and develop your technical and soft skills.

Job Description

• To process, sort and analyse large amounts of data and raw information to identify best practice, best process, best data, insights and find patterns that will help improve our data, data platform, client delivery and organisation as a market leader. 
• Build data products that offer real time value and extract valuable business insights to support client management, client deliveries and overall better decision making. Apply analytical and statistical expertise with data analysis and their related methods to analyse, interpret and communicate data outputs, events and patterns. Employ methods and models drawn from several areas within the context of mathematics, statistics, information science, and computer science. Critical thinking and problem-solving skills for interpreting data and supporting client delivery. A passion for machine-learning and research. To completely understand the data and processes that drive our business and add value to our business and our clients.
   

Qualifications

• BSC/BA in Computer Science 

• 3 to 5 years work experience

• Microsoft SQL certification.

Additional Information

What you’ll need to bring to the party

• Proven experience as a Data Scientist or Data Analyst.

• Experience in data mining. 

• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.

• Knowledge of Microsoft SQL Server, T-SQL, Azure and Python.

• Familiarity using business intelligence tools (Tableau). 

• Analytical mind and business acumen. 

• Strong math skills (e.g. statistics, algebra). 

• Problem-solving aptitude. 

• Excellent communication and presentation skills. 

• Ability to work independently and with team members from different backgrounds.

• Excellent attention to detail.

• A drive to learn and master new technologies and techniques.

• Working with large datasets and moving of files between different environments. 

• Import and Export data from different database technologies.

• Process automation and monitoring.

• Code migration from legacy languages and systems.

• Data Cleansing

• Data profiling

What you’ll be doing 

• Identify valuable data sources and automate collection, loading and transforming processes.

• Developing new datasets.

• Efficient Data Engineering (ETL).

• Data investigations and anomaly detection in data.

• Manage scheduled client and business processes and deliverables.

• Undertake pre-processing of structured and unstructured data. 

• Analyse large amounts of information to discover trends and patterns. 

• Build predictive models and machine-learning algorithms.

• Provide rapid feedback and insight on ad-hoc requests.

• Present information using data visualization techniques. 

• Propose solutions and strategies to business challenges. 

• Collaborate with client and product development teams.

• Undertaking data collection, pre-processing and analysis. 

• Building models to address business problems. 

• Code migration from legacy languages.

• Creating reports and presentations for business uses.

• Correlating similar data to find actionable results.

• Anomaly detection in data.

• Managing and administrating monthly business processes.

• Client (Internal/External) Communication.