Senior Expert Data Science

Understand complex and critical business problems, formulates integrated analytical approach to mine data sources, employ statistical methods and machine learning algorithms to contribute solving unmet medical needs, discover actionable insights and automate process for reducing effort and time for repeated use. To manage the implementation and adherence to the overall data lifecycle of enterprise data from data acquisition or creation through enrichment, consumption, retention, and retirement, enabling the availability of useful, clean, and accurate data throughout its usefull lifecycle. High agility to be able to work across various business domains.High agility to be able to work across various business domains. Integrate business presentations, smart visualization tools and contextual storytelling to translate findings back to business users with a clear impact.Independently manage budget, ensuring appropriate staffing and coordinating projects within the area. If managing a team: empowers the team and provides guidance and coaching, with initial guidance from more senior leaders supervised. This is usually their first people manager experience .

• Develop automation for repeatedly refreshing analysis and generating insights and collaborates with globally dispersed internal partners and cross-functional teams to solve critical business problems and deliver successfully on high visibility critical initiatives
• Understand life science data sources including sales, contracting, promotions, social media, patient claims and Real-World Evidence and Quickly learn the use of tools, data sources and analytical techniques needed to answer a wide range of critical business questions
• Articulate solutions/recommendations to business users. Works with senior data science team member to present analytical content concisely and effectively
• Manage own tasks and works with allied team members; plans proactively, anticipates and actively manages change, sets partner expectations as required, identifies operational risks and independently drives issues to resolution, minimizes surprise customer concerns

• Independently identifies research articles and reproduce/apply methodology to Novartis business problems and has high learning agility and diligently follows updates in industry and area of work

• Quality of insights generated, and solutions provided, with quantified business impact / return on investment, Effective communication with NBS CONEXTS and Country/Regional/Global partners
• Complete agreed targets for self-define and implement development plans for potential Subject Matter Experts and find creative ways to build team capabilities and play a direct role in driving a culture of innovation. Values and Behaviors: in line with leadership standards of Novartis
• Extensive experience required in: Statistical and Machine Learning techniques like Regression (esp, GLM, non-linear, etc.), Classification (CART, RF, SVM, GBM, etc.) Clustering, Design of Experiments, Monte Carlo Simulations, Statistical Inference, Feature Engineering, Time Series Forecasting, Text Mining and Natural Language Processing
• Good to have skills: Stochastic models, Bayesian Models, Markov Chains, Dynamic Programming and Optimization techniques, Deep Learning techniques on structured and un-structured data, Recommender Systems (content and collaborative filtering), etc.

• Masters / PHD (or bachelor s from a top Tier University) in a quantitative subject area (e.g. Statistics, Economics, Mathematics, Computer Science, Bioinformatics, Ops Research, etc.)
• 6+ years of proven experience in Data Science experience in commercial pharma would be a bonus. Handson to SAS, Python (Programming Language), R (Programming Language), SQL. Exposure to dashboard or web-apps building using Qlik sense, R-Shiny, Flask, Apache Hadoop. Applied Mathematics. Big Data, Data Governance, Data Literacy, Data Visualization, Deep Learning, Machine Learning, Machine Learning Algorithms, Master Data Management, Proteomics, and Statistical Modeling.

Apache Hadoop, Applied Mathematics, Big Data, Curiosity, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Deep Learning, Machine Learning (Ml), Machine Learning Algorithms, Master Data Management, Proteomics, Python (Programming Language), R (Programming Language), Statistical Modeling