Manager /Senior Manager - Data Scientist

Our client is an American multinational pharmaceutical and biotechnology corporation headquartered at New York City

It develops and produces medicines and vaccines for immunology, oncology, cardiology, endocrinology, and neurology

Provider of pharmaceutical products for multiple disease treatments. It has developed a novel cancer medicine that inhibits blood supply in tumor cells and destroys cellular reproduction.

About the role :

We are looking for a Sr Manager, Data Science and AI who will be responsible for delivering data-driven insights and/or AI-powered analytics tools to our client's Commercial organization and will either support a brand or therapeutic area or be part of a team designing, delivering, and upgrading innovative capabilities. 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.

RESPONSIBILITIES:

- Product / Brand and Therapeutic Area (TA) Insights

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

- Influence 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.

- Continuously evaluate and enhance existing brand /TA data science capabilities, identifying opportunities for optimization and innovation to drive greater business impact and ROI.

- 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

- 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 US Commercial 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.

Innovative Data Science Capabilities :

- Lead the design, delivery, and scaling of innovative solutions across the organization - from pilot phases to full-scale implementation.

- Drive 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.

- Steer the upgrading and refining capabilities based on feedback and insights gathered during pilot phases, continuously enhancing the effectiveness and relevance of implemented solutions.

- Play a key role in the organizational transformation by championing innovation, fostering a culture of experimentation, and facilitating the adoption of new capabilities at scale.

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 & EXPERIENCE:

- Minimum of Bachelor's degree with 7+ years of experience, preferably in Engineering, Economics, Statistics, Computer science, or related quantitative field.

- Advanced degree with 5+ years of experience in Applied Econometrics, Statistics, Data Mining, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field preferred.

- Extensive experience using Data Science models to solve problems in a business environment setting.

Relevant Experience:

- Extensive 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

- Strong experience with machine learning technology, such as: big data stack, Python, R, and visualization techniques

- Deep understanding of artificial intelligence concepts, experience with AI frameworks and libraries such as TensorFlow or PyTorch is also valuable.

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

- Extensive experience in management of secondary data with application to real-world data.

- Proven ability to connect, integrate and synthesize analysis and data into a meaningful 'so what' to drive concrete strategic recommendations for brand tactics.

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

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

PROFESSIONAL CHARACTERISTICS:

- Cross-functional influence: Expert in creating strategic direction and velocity through winning and influencing business partners to make complex decisions that influence multiple business areas.

- 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.

- 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.

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

- Exceptional Communicator: Can understand, translate, distill and present 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.

- Thought Partnership: 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.

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

- 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.

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