Manager - Advanced Analytics

Job Description Summary

-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 useful 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.  

Job Description

Major accountabilities:

• Project manage own tasks and works with allied team members; plans proactively, anticipates and actively manages change, sets stakeholder expectations as required, identifies operational risks and drives issues to resolution, minimizes surprise escalations, with initial support from supervisors -Collaborate with internal stakeholders, external partners and cross-functional teams to solve critical business problems -Understand life science data sources including preclinical, clinical, sales, contracting, promotions, social media, patient claims and Real World Evidence -Research and co-develop with supervisor in data management, new algorithms, methods, statistical models and business models and providing insight into structured and unstructured data, -Quickly learn the use of tools, data sources and analytical techniques needed to answer a wide range of critical business questions -Support the evaluation of the need of technology and novel scientific software, visualization tools and new approaches to computation to increase efficiency and quality of the Novartis practices -Independently identifies research articles and propose them for application to Novartis business problems -Develop automation for data management, repeatedly refreshing analysis and generating insights -Articulates solutions /recommendations to business users.
• Works with senior data science team member to present analytical content concisely and effectively -Ability to provide understandable and actionable business intelligence for key stakeholders -May lead a small team 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)

Key performance indicators:

• Achievement and governance endorsement of data management, advanced analytics program tollgates -Successful implementation based on KPI’s and data management, advanced analytics program objectives  

Minimum Requirements:
Work Experience:

• In-depth mastery of external environment and trends.
• 5+ years of relevant experience in Data Science.
• Track record delivering global solutions at scale.
• Has a strong professional network across Academia.

Skills:

• 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.

Languages :

• English.

Skills Desired

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