Overall:
- Ability to work with globally located stakeholders to understand business problem, available data and how both can be brought together to generate insights
- Co-ordinate with different teams to develop custom alogrithms, implement and monitor
- Good communication and interpersonal skills
- Experience of applying data science in life sciences or pharma industries will be an added advantage
Must have technical skills:
- Descriptive statistics
- Probability & probability distributions (Gaussian/non-Gaussian)
- Regression (linear/GLM/non-linear) techniques
- Classification techniques (Tree based methods - decision trees, random forests, etc., bagging, boosting, SVM)
- Unsupervised learning - clustering (K-means, hierarchical, DBScan)
- Coding: SAS, Python, R, SQL (Basics)
Good to have technical skills:
- Text mining/NLP
- Neural networks (ANN, RNN, CNN)
- Network analysis
- Coding/Cloud: SQL (advanced), Javascript, AWS/Azure
Background:
- Masters/PhD in Statistics, Mathematics, Economics, Computer Science, QR-OR
- If BTech, then from Tier 1 colleges
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