Data mining Specialist -Qatar Telecom

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QTEL1495 - Data mining Specialist

Business Unit: Consumer Business Field: Not Applicable Contract Type: Indefinite Closing Date: 30-Sep-2008

Overview: Qatar Telecom (Qtel) is proud to be a full-service telecom provider for the people of Qatar and increasingly, as it expands its operations into overseas markets, for its customers around the world. Our vision - "To be among the top 20 telecommunications companies in the world by 2020" - is succinct, ambitious and achievable. At Qtel, we are committed to rewarding, nurturing and retaining talent to help our employees achieve their potential. We have institutionalised global best HR practices to identify the potential of our employees and help them hone that talent by offering them a structured career path, counselling, training and development. We are looking for the best-in-class talent to drive our business and propel our growth. Join us if you are looking for a job that makes the most of your talents.

Accountabilities: Role & Context:Purpose: The Customer Acquisition and retention teamis seeking a focused, results-oriented individual to join a group of high-performing marketing professionals. The successful candidate will complement the team by working across the business unit to build and deliver decision making solutions which guide successful acquisition, growth, and retention initiatives Context: The position is in the Qtel Customer Acquisition and retention team as regards development and addressing initiatives ROLE ACCOUNTABILITIES: The Data mining specialist will be exposed to a broad range of activities within the customers Acquisition & Retention department. The position encompasses the following major functions: Execute customer analytics & data mining projects from start to finish including project management, data manipulation, preparation, modeling, validation and communication of key insights. Provide Analytical Services where needed to improve segment performance and enhance customer experience within the company. Conduct extensive mining of large databases to analyze marketing programs performance, productivity by segment and generate detailed reports that drive insight and highlight opportunities for incremental revenue, churn improvement, and increased customer satisfaction. Develop statistical and predictive models for different marketing initiatives (ie. Customer segmentation, churn, cross sell, fraud, customer lifetime value, customer profitability, campaigns response rate, customer potential & future value, Social network size & value) by using different data mining tools (SAS enterprise miner, KXen, SPSS Clementine) Apply statistical and data mining techniques such as Decision Trees, Logistic Regression, clustering and Neural Network, Time series Analysis to make recommendations for Go-to-Market initiatives Develop and effectively present, graphically and verbally, analysis of data that support developed hypothesis(product and service usage analysis, life time value estimation, pricing analysis, service channel usage analysis, sales optimization & performance tracking, claims & payments analysis, next product recommendation, etc.) Prepare ad-hoc analyses as required (ARPU, MOU, churn, cross/up sell, migration, acquisition, post campaign evaluation, etc). Work with business partners in Marketing to clarify data needs and objectives. Ability to translate business problem into data mining problem and marketing strategies/initiatives into actionable database marketing deliverables. Liaise with IT to resolve data issues and ensure proper operation of data warehouse. Provide expert data services to Qtel business specialists/managers this includes the preparation of reports that drive insight and highlight opportunities for incremental revenue, churn improvement, and increased customer satisfaction. Liaise with the Acquisition & retention team, Multimedia team, IT group and other cross-functional groups. Provide fact based recommendations and timely information to all levels of the organization from analyst peers to executives.

KEY PERFORMANCE INDICATORS: Key measures of success include: Supporting on a regular basis product managers Addressing marketing team data needs. Control & reduction of churn rate. Increase of ARPU. Increased customer satisfaction index

EXPERIENCE AND QUALIFICATIONS: Minimum Experience & Essential Knowledge Strong quantitative background (applied mathematics/statistics, engineering, etc); Experience with data mining techniques (e.g. decision trees, neural networks, regression analysis, time series analysis, clustering, association rules, etc.); Analytical software such as SAS Enterprise Miner, SPSS, Clementine; Strong analytical and problem solving skills; Excellent written and oral communication skills; Data warehouse, data mart and databases experience Minimum Entry Qualifications At least 7 years experience working in an advanced Data mining/ modeling / data warehousing environment Possession of a university degree; Prior experience in telecom industry. Perfect written and spoken English

SKILLS: Technical & non technical Experience in gathering detailed data requirements, working with large data sets and data mining techniques. Experience should include data cleansing, partitioning, compiling and manipulation, for purposes of business and statistical analysis; Ability to perform drill down analysis and apply business knowledge to solve problems and prove hypothesis Experience in data preparation, predictive modeling, scoring, and strategic deployment of analytics results (ie. Segmentation model, Potential churn modeling) to provide maximum business value to Qtel Internal customers. Demonstrated understanding of business with strong analytical and problem solving skills Other Skills Expertise in Statistical software (SAS enterprise miner, Clementine, etc.) Expert in non web programming languages (SAS, KXEN, SPSS) Relational database knowledge, superior spreadsheet formatting and creation of templates; Strong PC Skills - MS Access, PowerPoint and Excel Due to huge volume of applications received, only shortlisted candidates would be contacted for interviews.