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Manager , Data Science & AI

Pfizer -
São Paulo, SP

Candidate-se Agora

Detalhes da vaga

Tempo integral
Há 7 horas

Qualificações

Estatística
Data Mining
Gestão
Big Data
NoSQL
Tableau
Java
SQL
Análise Estatística
Graduação
Machine Learning
Scala
Angular
Visualização de Dados
Inteligência artificial
Liderança
Python

Descrição completa da vaga

The Data Science & AI team with Global Commercial Analytics organization is committed to transforming data into actionable intelligence, empowering Pfizer to stay competitive and innovative in today's data-driven landscape. We play a pivotal role in extracting insights from large and complex datasets to drive strategic decision-making. Collaborating closely with various subject matter experts across various fields, our team leverages advanced statistical analysis, machine learning techniques, and data visualization tools to uncover patterns, trends, and correlations within the data. Additionally, we are dedicated to delivering new, innovative capabilities by deploying cutting-edge machine learning algorithms and artificial intelligence techniques to solve complex problems and create value.

We are looking for a Manager, Data Science and AI who will be responsible for delivering data-derived insights and/or AI-powered analytics tools to Pfizer’s Commercial organization and will support a brand or therapeutic area. This includes leading the execution and interpretation of AI/ML models, framing problems, and shaping solutions with clear and compelling communication of data-driven insights.

This role is dynamic, fast-paced, highly collaborative, and covers a broad range of strategic topics that are critical to our business. The successful candidate will join CAAI colleagues worldwide that are driving business transformation through proactive thought-leadership, innovative analytical capabilities, and their ability to communicate highly complex and dynamic information in new and creative ways.

ROLE RESPONSIBILITY

Product / Brand and Therapeutic Area (TA) Insights

Deliver advanced analytical models, predictive algorithms, and AI-powered tools to extract actionable insights to drive Commercial strategies and tactics.
Support the end-to-end delivery of data science insights, from framing the business question, designing the solution, and delivering recommendations.
Break down technical concepts into digestible insights and guide diverse stakeholders how to interpret.
Build strong relationships with key stakeholders, effectively communicating the value proposition of data science.

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

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.
Deliver consolidated insights and actionable recommendations to Commercial to teams, ensuring alignment with strategic objectives and insights findings.
Represent data science function and capabilities in Analytic POD meetings.
Work closely with cross-functional teams to ensure seamless integration of brand analytics insights into decision-making processes and strategic initiatives.

Cross-Functional Collaboration

Work closely with Analytics Engineering to ensure the data ecosystem is conducive for data science modeling purposes.
Partner with Digital teams to enhance data science capabilities, aligning efforts to leverage digital data sources effectively.
Foster collaboration with other teams to ensure seamless integration of data science initiatives across the organization's infrastructure, promoting efficiency and effectiveness in leveraging data for informed decision-making.

Qualifications

Minimum of bachelor’s degree with 3+ years of experience, preferably in engineering, economics, statistics, computer science, or related quantitative field.
Advanced degree preferred with 0~2 years of experience in Applied Econometrics, Statistics, Data Mining, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field preferred.
Experience using data science models to solve problems in an education or business environment setting.

Relevant Experience

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.
Experience with machine learning technology, such as: big data stack, Java, Python, R, Scala and visualization techniques, including Dash, Tableau and Angular.
Experience in understanding brand content, strategy, and tactics
Ability to effectively utilize dashboards and data products to derive insights.
Experience with supporting commercial strategies and tactics, experience in pharmaceutical or healthcare industry is preferred.
Experience in management of secondary data with application of real-world data.
Ability to partner with cross-functional teams (Commercial, Medical, Operations) to execute brand tactics.
Able to connect, integrate and synthesize analysis and data into a meaningful ‘so what’ to drive concrete strategic recommendations for brand tactics.
Capable of describing relevant caveats in data or in a model and how they relate to business question.
Ability to be flexible, prioritize multiple demands and deal with ambiguity.

PROFESSIONAL CHARACTERISTICS

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.
Data and Information Manager: Understands and uses analytical skills/tools to produce data in a clean, organized way to drive objective insights.
Communication: 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.
Collaborative: Manages projects with and through others; shares responsibility and credit; develops self and others through teamwork.
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.
Self-Starter: Takes an active role in one’s own professional development; stays abreast of analytical trends, and cutting-edge applications of data.

OTHER JOB DETAILS

Work Location Assignment: On-site 2-3x/week or as needed by the business
Last day to apply for job: June 22

EEO (Equal Employment Opportunity) & Employment Eligibility

Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, or disability.

To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI-use guidelines available on Pfizer Careers.

Marketing and Market Research

Candidate-se Agora

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ROLE RESPONSIBILITY

Product / Brand and Therapeutic Area (TA) Insights

Deliver advanced analytical models, predictive algorithms, and AI-powered tools to extract actionable insights to drive Commercial strategies and tactics.

Support the end-to-end delivery of data science insights, from framing the business question, designing the solution, and delivering recommendations.

Break down technical concepts into digestible insights and guide diverse stakeholders how to interpret.

Build strong relationships with key stakeholders, effectively communicating the value proposition of data science.

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

Cross-Functional Collaboration

Work closely with Analytics Engineering to ensure the data ecosystem is conducive for data science modeling purposes.

Partner with Digital teams to enhance data science capabilities, aligning efforts to leverage digital data sources effectively.

Foster collaboration with other teams to ensure seamless integration of data science initiatives across the organization's infrastructure, promoting efficiency and effectiveness in leveraging data for informed decision-making.

Qualifications

Minimum of bachelor’s degree with 3+ years of experience, preferably in engineering, economics, statistics, computer science, or related quantitative field.

Advanced degree preferred with 0~2 years of experience in Applied Econometrics, Statistics, Data Mining, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field preferred.

Experience using data science models to solve problems in an education or business environment setting.

Relevant Experience

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.

Experience with machine learning technology, such as: big data stack, Java, Python, R, Scala and visualization techniques, including Dash, Tableau and Angular.

Experience in understanding brand content, strategy, and tactics

Ability to effectively utilize dashboards and data products to derive insights.

Experience with supporting commercial strategies and tactics, experience in pharmaceutical or healthcare industry is preferred.

Experience in management of secondary data with application of real-world data.

Ability to partner with cross-functional teams (Commercial, Medical, Operations) to execute brand tactics.

Able to connect, integrate and synthesize analysis and data into a meaningful ‘so what’ to drive concrete strategic recommendations for brand tactics.

Capable of describing relevant caveats in data or in a model and how they relate to business question.

Ability to be flexible, prioritize multiple demands and deal with ambiguity.

PROFESSIONAL CHARACTERISTICS

Data and Information Manager: Understands and uses analytical skills/tools to produce data in a clean, organized way to drive objective insights.

Communication: 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.

Collaborative: Manages projects with and through others; shares responsibility and credit; develops self and others through teamwork.

Self-Starter: Takes an active role in one’s own professional development; stays abreast of analytical trends, and cutting-edge applications of data.

OTHER JOB DETAILS

Work Location Assignment: On-site 2-3x/week or as needed by the business

Last day to apply for job: June 22