Description
Role Overview:
- The person will be part of data science team for a major US insurance client.
- Doing independent research, analyze, and present data as assigned.
- Developing statistical models in SAS, Python/R.
- Develop detailed SAS/Python/R codes for data preparation and model scoring to be used in production.
- Critically examine and deep dive into models and improving model performance.
- Prepare detailed documentation of models output, performance and all the steps involved.
- Assist in learning and development of new team members.
- Identify and participate in continuous improvement initiatives.
Key Responsibilities:
- Complete understanding of business objective/problem statement as well as modeling goals.
- Experience in data extraction and data manipulation in SAS, Python/R and Excel.
- Data exploration, data preparation (outlier treatment, missing value imputation, variable transformation etc.), model building, model validation, optimization and scoring using SAS/Python/R.
- Preparing detailed documentation of models output results (of different iterations), model performance, feature importance and all the steps involved to optimize the model performance.
- Adhere to project deliverables and controls.
- Experience in data control and data automation.
- Good written and verbal communication skills.
Candidate Profile:
- Bachelor- s/Master's degree in computer science, mathematics, statistics.
- 2-7 years- experience, preferably in data science/marketing analytics/building predictive models.
- Should be really proficient in building predictive statistical models (look alike/response models).
- Should have experience working on techniques like Logistic Regression, Decision Trees, Random Forest, GBM, XG-Boost, K-Means Clustering etc.
- Superior analytical and problem solving skills.
- Ability to take initiative, quick learner and innovative.
- Outstanding written and verbal communication skills.
- Able to work in fast pace continuously evolving environment and ready to take up uphill challenges.
- Is able to understand cross cultural differences and can work with clients across the globe.
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