Senior Specialist Data Science -- IA&IO

Job Description

Position Description:

This position will be responsible for supporting the marketing analytics in partnership with the business team with particular focus on promotional investment optimization. As a Senior Data Scientist, you need to have excellent quantitative and analytical skills to combine the art and science of data analytics and marketing to help business stakeholders address the complex challenges of marketing effectiveness, ROI, brand equity impact, channel effectiveness, and pricing impact. In this role you’ll work with cross-functional teams dealing with customer engagements and marketing leadership. The role delivering an evolving stream of value and enabling fact-based decision making throughout the lifecycle of the product. This role will be accountable to support brands with go-to-market strategies and customer analytics with a focus on making analytics solutions scalable. We're looking for a strong, technically sound in pharma Data Sets who is interested in working within a challenging environment.

Primary activities include, but are not limited to:

• Deep knowledge of Marketing Analytics. Should be an SME in Pharma Marketing Analytics - ROI, Clustering, Test & Control, MMX modeling, Campaign management

• Build and analyze behavioral segments, Promotional Response models, Return on Investments, impact assessment for physician- and patient-directed promotional programs and Marketing Mix models, Optimal promotional sequences to determine business impacts of various Health Care Provider (HCP) and Health Care Consumer (HCC) promotions.

• Communicate effectively with cross-functional teams and internal clients such as marketing brand leaders, center of excellence teams, senior management etc., to stay abreast of business trends, understand the business issues and develop relevant business intelligence and analytical solutions.

• Opportunities to improve go-to-market strategy by gaining additional insight into customer interactions and sentiment

• Lead / Hands on with Statistical and Machine Learning concepts. ML techniques and their applications to business and customer analytics problems

• Generate standard or custom reports and presentations summarizing business and financial data for review by executives, managers, clients, and other stakeholders.  

• Design and build software tools to streamline statistical and operations research based advanced analytical methods.

• Analyze industry and technology trends to identify target markets for launch products or to improve sales of existing products. Research and apply emerging analytical methods and tools such as Machine Learning, Deep Learning, Advanced Statistical methods, Cloud Computing in Amazon Web Server (AWS), Python, R etc., to measure promotional impacts and optimal budget allocations.

• Deep understanding of internal customers' data and analytical needs focusing on the customer-facing model requirements

Qualifications:

• Minimum 4-6 years of experience with an advanced degree (MS, PhD) in a quantitative science or related discipline of engineering/economics/statistics.

• Experience in marketing analytics and application of advanced methods on large and disparate datasets, specifically: Modelling and analytics: Design of Experiments, Time Series, Regression methods.

• Data Mining, Predictive Modelling & Machine Learning algorithms. Optimization & Simulation.

• Knowledge of analyzing sales impact of promotional campaigns such as Linear TV, Streaming TV, Over-the-Top (OTT), Radio, Mobile, Display, Social, Point of Care, Paid & Organic search etc. that are directed at Health Care Consumers (HCC) is required.

• Relevant expertise in using analytic tools such as Excel, SAS, SQL, Python, R, ML

• Understanding of the Health Care or Pharmaceutical industry and knowledge of using various 3rd party data sources, such as IMS Exponent and/or Longitudinal Patient Level Data are necessary. 

• Strong analytical skills, excellent communication skills and the ability to communicate actionable analytical findings to a non-technical audience in clear and concise language.

• Knowledge of the application of statistical estimation techniques and / or optimization techniques

• Exposure to working with large data sets

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