Data Science Strategist

3 weeks ago

Mumbai, Maharashtra, India Pfizer Full time
About the Role

The Commercial Analytics & AI (CAAI) organization at Pfizer is seeking a highly skilled Sr. Manager, Data Science and AI to join our team of experts dedicated to transforming data into actionable intelligence.

This dynamic and fast-paced role will involve leading the execution and interpretation of AI/ML models, framing problems, and shaping solutions with clear and compelling communication of data-driven insights.

Responsibilities:

  1. Deliver advanced analytical models, predictive algorithms, and AI-powered tools to extract actionable insights to drive US Commercial strategies and tactics.
  2. Engage with the end-to-end delivery of data science insights, from framing the business question, designing the solution, and delivering recommendations.
  3. Break down technical concepts into digestible insights and guide diverse stakeholders how to interpret.
  4. Continuously evaluate and enhance existing brand/TA data science capabilities, identifying opportunities for optimization and innovation to drive greater business impact and ROI.
  5. Build strong relationships with key stakeholders, effectively communicating the value proposition of data science and fostering a culture of data-driven decision-making.

Collaborate Cross-Functionally as a Brand/TA Focused Analytics POD

  1. Collaborate within the analytics POD, coordinating efforts with the Insight Strategy & Execution and Market Research Insights counterparts to develop and execute a comprehensive brand analytics plan.
  2. Deliver consolidated insights and actionable recommendations to US Commercial teams, ensuring alignment with strategic objectives and insights findings.
  3. Represent data science function and capabilities in Analytic POD meetings.
  4. Work closely with cross-functional teams to ensure seamless integration of brand analytics insights into decision-making processes and strategic initiatives.

Innovative Data Science Capabilities

  1. Support the design, delivery, and scaling of innovative solutions across the organization – from pilot phases to full-scale implementation.
  2. Collaborate on the delivery process, leveraging agile methodologies and best practices to efficiently progress from pilot projects to scalable solutions, while maintaining a focus on quality and innovation.
  3. Actively participate in upgrading and refining capabilities based on feedback and insights gathered during pilot phases, continuously enhancing the effectiveness and relevance of implemented solutions.
  4. Play a key role in the organizational transformation by facilitating the adoption of new capabilities at scale.

Qualification & Experience:

  1. Bachelor's degree with 7-12 years of experience, preferably in Engineering, Economics, Statistics, Computer Science, or related quantitative field.
  2. Advanced degree with 3+ years of experience in Applied Econometrics, Statistics, Data Mining, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field preferred.
  3. Experience using Data Science models to solve problems in a business environment setting.

Relevant Experience

  1. Experience with both traditional SQL and modern NoSQL data stores including SQL, and large-scale distributed systems such as Hadoop and or working in Snowflake/Databricks.
  2. Experience with machine learning technology, such as big data stacked Java, Python, R, Scala and visualization techniques, including Dash, Tableau and Angular.
  3. Experience in understanding brand content, strategy, and tactics.
  4. Ability to effectively utilize dashboards and data products to derive insights.
  5. Experience with supporting commercial strategies and tactics, experience in pharmaceutical or healthcare industry is preferred.
  6. Experience in management of secondary data with application of real-world data.
  7. Ability to partner with cross-functional teams (Commercial, Medical, Operations) to execute brand tactics.
  8. Able to connect, integrate and synthesize analysis and data into a meaningful 'so what' to drive concrete strategic recommendations for brand tactics.
  9. Capable of describing relevant caveats in data or in a model and how they relate to business question.
  10. Ability to be flexible, prioritize multiple demands and deal with ambiguity.

Professional Characteristics:

  1. Growth Mindset: Evaluates, understands and communicates the impact of certain data insights across the business and works to assist business partners foresee potential strategic changes.
  2. Analytical Thinker: Understands how to synthesize facts and information from varied data sources, both new and pre-existing, into discernable insights and perspectives; takes a problem-solving approach by connecting analytical thinking with an understanding of business drivers and how CAAI can provide value to the organization.
  3. Strong Data and Information Manager: Understands and uses analytical skills/tools to produce data in a clean, organized way to drive objective insights.
  4. Strong Communicator: Can understand, translate, and distill the complex, technical findings of the data science team into commentary that facilitates effective decision making; can readily align interpersonal style with the individual needs of others.
  5. Relationship Manager: Acts as a thought partner and brings forward recommendations/questions that influences stakeholders; builds robust and long-term strategic relationships with individuals from all levels of the organization, understanding individual goals and objectives to ensure future alignment of the entire portfolio.
  6. Highly Collaborative: Manages projects with and through others; shares responsibility and credit; develops self and others through teamwork.
  7. Strong Project Manager: Clearly articulates scope and deliverables of projects; breaks complex initiatives into detailed component parts and sequences actions appropriately; develops action plans and monitors progress independently; designs success criteria and uses them to track outcomes; drives implementation of recommendations when appropriate, engages with stakeholders throughout to ensure buy-in.
  8. Proactive Self-Starter: Takes an active role in one's own professional development; stays abreast of analytical trends, and cutting-edge applications of data.

Organizational Relationships:

  1. US Commercial Brand/TA Teams
  2. Data Science and AI Leadership Team
  3. Closely collaborate with Analytics Engineering, Insight Strategy & Execution, and Market Research Insights team
  4. Data Science counterparts in Digital Organization
  5. Chief Marketing Office across innovative data-science driven capabilities

Financial Accountability

  1. Manage budget and spending for CAAI Data Science projects (including contract resources)

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