Data Scientist-Insights Manager- U.S. Pharma MNC

Contract Type
Academic title
Job description

Define, drive and implement data and analytics strategy and governance:

  • Develop, implement, and refine core marketing measurement frameworks and models (e.g., attribution, engagement, churn, ROI, experiment methodology, segmentation)
  • Source valuable data from a wide range of systems, including real-time and large-scale sources, and automate collection processes to build predictive models and machine-learning algorithms; and combine models through ensemble modeling
  • Work closely with agencies and teams to define the layout for backend architecture and storage.
  • Monitor cloud storage and provide recommendations to optimize storage and its usage.
  • Lead cost-effective storage and retrieval of data by leveraging data processing technologies (e.g., Spark) and programming languages (e.g., Python)
  • Identify key metrics that drive the performance of marketing channels and marketing products, make accessible to cross-functional partners using data visualization, dashboards and reports, and drive metrics through statistical analyses of experiments

Develop actionable insights that can be leveraged by cross functional teams:

  • Analyze large amounts of information to discover trends and patterns to understand how audiences engage with digital and traditional content
  • Decode the behaviors embedded deep into data sets using machine learning and advanced statistical algorithms
  • Conduct exploratory analysis using a variety of tools and languages (e.g., SQL, SAS, R, Python, Alteryx, TOAD, Power BI, Tableau, AWS, Azure, BigQuery, Redshift)
  • Lead creative, hypothesis-based exploration of data against a wide cultural and human context (backdrop) to drive innovation and insights
  • Able to derive the “so what” from models and support the development of compelling stories that facilitate action and drive growth.
  • Transform raw data into meaningful and impactful analysis characterized by strong data governance, technique clarity, and clear documentation

Data Management & Curation:

Revision A

  • Design, review and monitor optimal approach for data quality assessment for complex or large projects applying extensive knowledge of the data, potential issues such as missing values, duplicates and inconsistent formats, and the implications for the data science/analytics process.
  • Creates novel sources of potential data to reflect real-world observations and drive differentiating insights for Abbott Nutrition brands

Stakeholder Relationship & Project Management:

  • Drive alignment of measurement strategy in the Nutrition Category.
  • Develop and cultivate relationships with all relevant partners at the regional and local level.
  • Partner with Affiliates on overall measurement frameworks and models to approach & recommend further collaboration initiatives across the region.
  • Plans, proposes, initiates, enhancements and executes data science projects using best-in-class project management practices
  • Anticipates, mitigates, and manages obstacles, resistance, and conflict that might compromise timelines and delivery
  • Develops support tools and methodologies as required, to enable effective project execution in line with objectives

Tracking of digital, CRM and ecommerce performance:

  • Put in place an effective system to track sales, share and operational costs across digital marketing, CRM and ecommerce
  • Leverage the data captured from digital marketing, CRM and ecommerce channels to provide inputs to both the Affiliates and the Area team on areas around ROI measurement, KPIs tracking, etc.

Training & Development

  • Increased data and analytics knowledge/savvy throughout the Marketing organization (not exclusive to analytics team)
  • Support the skills’ development, knowledge and competence of the Area and Affiliate teams in order to effectively execute their activities and gain competitive advantage.
  • Identify and share best practices across Area and Affiliates.

Develop external partners, whose expertise can be leveraged to develop our skills and capabilities in

  • Deep expertise in data science, machine-learning and marketing - Robust experience applying data science concepts to marketing (i.e., marketing I/O models) - Expertise in PowerBI - Familiarity with R, SQL, Python, Scala, Java or C++,, Google Analytics, Facebook/Google advertising, etc. - Strong math skills (i.e., statistics, algebra), analytical thinking and business acumen - Strong data visualization skills and the ability to translate and communicate analytics and insights to non-technical audiences

Excellent Compensation

  • Global Exposure
  • Visible Career Path
  • General Shift
Other notes
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