Data Scientist - IT

20+ brands catering to 50+ disease areas! The team of Novartis specialists within Insights & Analytics are on a data and digital transformation journey, leveraging analytics to generate actionable insights for Novartis medicines impacting more than 500 million patients worldwide. The team is poised to enable easier, faster and reliable decisions for Novartis divisions across the globe.

- Work on a variety of business applications including but not limited to: Customer Segmentation & Targeting, Event Prediction, Propensity Modelling, Churn Modelling, Customer Lifetime Value Estimation, Forecasting, Recommender Systems, Modelling Response to Incentives, Marketing Mix Optimization, Price Optimization

- Develop automation for repeatedly refreshing analysis and generating insights Modelling Response to Incentives, Marketing Mix Optimization, Price Optimization

- Collaborates with globally dispersed internal stakeholders and cross-functional teams to solve critical business problems and deliver successfully on high visibility strategic initiatives

- Understands life science data sources including sales, contracting, promotions, social media, patient claims and Real World Evidence

- Quickly learns the use of tools, data sources and analytical techniques needed to answer a wide range of critical business questions

- Articulates solutions/recommendations to business users. Works with senior data science team member to present analytical content concisely and effectively

- Project manages own tasks and works with allied team members; plans proactively, anticipates and actively manages change, sets stakeholder expectations as required, identifies operational risks and independently drives issues to resolution, minimizes surprise escalations

- Independently identifies research articles and reproduce/apply methodology to Novartis business problems

Minimum requirements :

- Masters (or Bachelors from a top Tier University) in a quantitative discipline (e.g. Applied Mathematics, Computer Science, Bioinformatics, Statistics; can also consider Ops Research, Econometrics).

- 5+ years of relevant experience in Data Science with a minimum of 5 years post qualification experience (PhD considered as experience) - Extensive experience in Statistical and Machine Learning techniques like Regression (Linear/Logit/Gamma), Clustering (K-Means/Modes/Hier), Decision Trees, Text Mining and Natural Language Processing, Stochastic models, Bayesian Models, Markov Chains, Monte Carlo Simulations, Non-linear Time Series, Dynamic Programming and Optimization techniques, Design of Experiments, Neural Networks, Statistical Inference, Collaborative Filtering, Feature Engineering, etc.

- Extensive experience in working with large-scale datasets (in bigdata architecture, data lake, data mart, data warehouse). Demonstrated use of analytical packages and query languages such as SAS, R, SQL, SPSS, Matlab, Alteryx

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 algorithms, 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)

Good to have technical skills:

- Text mining/NLP

- Neural networks (ANN, RNN, CNN)

- Neural networks (ANN, RNN, CNN)

- Network analysis

Coding: SAS, Python, R, SQL (Basics)

Coding/Cloud: SQL (advanced), JavaScript, AWS/Azure