Job Description:
- Data Miner will be expected to develop statistical frameworks and create algorithms/logic to build models across different areas of work. This role involves analyzing business problems, creating statistical models, adherence to delivery timelines, creation and deliveryof presentations/documents/reports to stakeholders.
Responsibilities:
- Formulate business problems, create statistical hypothesis, understand statistical solutions/models and provide results & recommendations
- Validate and refresh models, verify extraction, transformation and load of data from different sources
- Provide project manager with status reports, business benefits on initiatives and activity roadmap aligned to broad project plan
- Timely delivery (coordinating with onsite/ off-site team depending on the need) of the project Ensuring high quality deliverables and smooth day-to-day operations of projects
- Providing inputs to the delivery manager
Technicalskills:
- Knowledge of statistics, analytical modelling is essential.
- Solid background in Business Intelligence with experience in Marketing and familiar with Customer lifecycle/ customer strategy/ CRM
- Experience in statistical and data analysis in one or more of the following areas is necessary
- Quantitative techniques like logistic regression, decision trees, linear regression, segmentation etc.
- Text Analytics
- Scorecard building
- Identifying and defining KPIs to track results, performance of operations, campaigns etc.
Campaign management:
- Hands-on experience in data handling and statistical tools like SQL, SPSS, SASetc, with excellent SPSS,SAS skills and knowledge of SAS E-Miner
- Experience with business intelligence tools
Other Requirements:
- Should be a fast learner and a self starter, open to learning new statistical tools.
- Must possess excellent communication, documentation and presentation skills (both verbal & written)
- Advanced Excel and PowerPoint skills
- Flexible and adaptable to change and ability to multitask
- Ability to wok independently with minimal supervision
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