Data Scientist - Pharma

Job Purpose:

- Understand complex and critical business problems from Country/Regional/Global business functions, formulate integrated analytical approach to mine data sources, employ statistical methods and machine learning algorithms to discover actionable insights and automate process for reducing effort and time for repeated use.

- High agility to be able to work across various business domains (commercial, NTO, GDD,NIBR, NBS) or divisions (Onco, GenMeds, Sandoz). Able to use business presentations, smart visualization tools and contextual story-telling to translate findings back to business users with a clear impact. Direct team management involved.

Responsibilities:

- 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

- 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 business problems

- 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) English

- 6+ years of relevant experience in Data Science with a minimum of 8 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.

- Demonstrated use of analytical packages and query languages such as SAS, R, SQL, SPSS, Matlab, Alteryx, Python