Associate Director Data Science

-Understands complex and critical business problems from a variety of stakeholders and business functions, formulate 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 definition, 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. Integrate business presentations, smart visualization tools and contextual storytelling to translate findings back to business users with a clear impact. Independently set strategy, manage budget, ensuring appropriate staffing and coordinating projects within the area supervised. If managing a team: empowers the team and provides guidance and coaching, with limited guidance from more senior managers.

• Innovate by transforming the way to solve a problem using Effective data management, Data Science and Artificial Intelligence. Articulates solutions /recommendations to business users. Provides pathways to manage data effective for analytical uses.
• Presents analytical content concisely and effectively to non-technical audiences and influences non-analytical business leaders to drive major strategic decisions basis analytical inputs -Coordinates, prioritize and efficiently allocates the team resources to critical initiatives
• plans resources proactively, anticipates and actively manages change, sets stakeholder expectations as required, identifies operational risks and enable the team to drives issues to resolution, balances multiple priorities and minimizes surprise escalations -Collaborates with internal stakeholders, external partners and Institutions and cross-functional teams to solve critical business problems, propose operational efficiencies and innovative approaches.
• Proactively evaluates the need of technology and novel scientific software, visualization tools and new approaches to computation to increase efficiency and quality of the Novartis data sciences practices -Provides agile consulting, guidance and non-standard exploratory analysis for unplanned urgent problem -Independently identifies research articles and reproduce / apply methodology to Novartis business problems -Publishes in peer reviewed journals, helps organizing sessions at external professional conferences and contributes to cross-industry work streams in external relevant working group
• Makes right choices from a breadth of tools, data sources and analytical techniques to answer a wide range of critical business questions -Ensures exemplary communication with all stakeholders including senior business leaders -Contribute to the development of Novartis data management and data science capabilities.
• May lead a Team / Function or in-depth technical expertise in a scientific / technical field depending upon the career path (Manager/Individual contributor) -Reporting of technical complaints / adverse events / special case scenarios related to Novartis products within 24 hours of receipt -Distribution of marketing samples (where applicable)
• Delivering projects and leading internal customer expectations across multi-channel marketing analytics, Portfolio Analytics, Targeting and Segmentation, Predictive Analytics, Resource Allocation and Optimization
• Degree of leadership provided to junior associates in adopting data science to day-to-day work.Effective communication with NBS CONEXTS. Country/Regional/Global stakeholders and Executes agreed targets for self and the team.

• 10-12 years of relevant experience in Data Science. In case of PhD, 6 years post qualification experience. Experience in commercial pharma would be an added bonus.
• 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 unstructured data, Recommender Systems (content and collaborative filtering), etc.
• Tools and Packages: SAS, R, Python, SQL. Exposure to dashboard or web-apps building using Qliksense, R-Shiny, Flask, etc. would be added advantage.
• Working very closely with business stakeholders on projects; interactions with business stakeholder on a daily basis and closely follow the business operations and the direct impact of work done. Has high learning agility and diligently follows updates in industry and area of work.
• A variety of projects - ranging from building boutique models from scratch to implementing industrial scale ML-based recommendation engines

• PhD or Masters (or Bachelors from a top Tier University) in a quantitative discipline (e.g. Statistics, Economics, Mathemat-ics, Computer Science, Bioinformatics, Ops Research, etc.).
• Min 10+ years of hands-on experience in analytics in marketing analytics and experience in pharma industry is preferable. Statistical Modeling/Machine Learning techniques like Regression (esp, GLM, non-linear, etc.). experience in any one of R or Python and exposure to SAS and SQL. Familiarity with Alteryx will be a bonus. Visualization tools - Qlikview, Qliksense, Tableau.
• Hands on to technologies as Apache Spark. Artificial Intelligence (Ai). Big Data, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Machine Learning (Ml), Master Data Management. Python (Programming Language), R (Programming Language) and Statistical Analysis

Apache Spark, Artificial Intelligence (AI), Big Data, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Machine Learning (Ml), Master Data Management, Python (Programming Language), R (Programming Language), Statistical Analysis