Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global healthcare and biotechnology organization is committed to improving patient outcomes and creating healthier communities through scientific innovation. Its Data, Digital, and Analytics team works across Commercial, Medical, and Government Affairs functions to address complex healthcare challenges through data, analytics, artificial intelligence, and machine learning.
Key Responsibilities
- Lead and guide the data science lifecycle for high-priority AI and machine learning initiatives.
- Serve as a subject matter expert in advanced analytical methodologies across data, analytics, and AI initiatives.
- Drive the end-to-end development, scaling, and deployment of AI solutions that optimize marketing efficiency, medical affairs strategies, and customer experiences.
- Develop and implement enterprise recommendation engines and advanced predictive and forecasting solutions.
- Act as a strategic technical advisor to senior leadership and cross-functional stakeholders.
- Conduct proactive research and apply advanced technical expertise to influence long-term business strategies.
- Apply machine learning, deep learning, causal inference, and advanced statistical modeling to complex structured and unstructured datasets.
- Collaborate with Data Science Product Owners, ML Engineers, and other technical teams to develop production-grade and scalable applications.
- Evaluate technical and business trade-offs and provide recommendations that support effective decision-making.
- Share technical perspectives and best practices to elevate the organization’s data science capabilities.
- Translate complex algorithmic architectures and predictive insights into clear and compelling business narratives.
- Communicate analytical findings effectively to non-technical stakeholders and drive informed operational action.
Required Qualifications
- Bachelor’s degree.
- 7+ years of hands-on experience in artificial intelligence, with a proven record of technical leadership, innovation, and independent project execution.
- 3+ years of dedicated experience applying recommender systems, including content-based and collaborative filtering approaches.
- 3+ years of experience developing predictive and forecasting models using enterprise-scale data.
- Deep proficiency in quantitative disciplines, including deep learning, causal inference, and advanced statistical modeling.
- Strong experience working with large-scale and complex data environments.
- Experience with big data platforms such as Hadoop and Spark.
- Experience working with cloud computing ecosystems, particularly AWS.
- Advanced proficiency in Python.
- Strong familiarity with open-source machine learning and agentic frameworks and libraries, including technologies such as LangChain, CrewAI, TensorFlow, and PyTorch.
- Ability to apply advanced AI and machine learning techniques to complex enterprise-level business problems.
- Strong communication, leadership, collaboration, and time management skills.
Preferred Qualifications
- Master’s or PhD in Data Science, Computer Science, Applied Mathematics, Statistics, or a related quantitative discipline.
- Experience working in highly regulated environments such as healthcare, pharmaceutical, or biotechnology organizations.
- Experience working with Commercial, Medical Affairs, claims, marketing, and longitudinal patient data.
- Demonstrated ability to apply transferable artificial intelligence and machine learning techniques across diverse business domains and ambiguous use cases.
- Experience using industry-standard visualization and analytics platforms such as Tableau, Qlik, or Data Studio.
- Experience translating complex analytical findings into accessible and actionable insights.
- Track record of open-source contributions, peer-reviewed publications, or patents.
- Strong ability to influence cross-functional stakeholders and communicate sophisticated technical concepts to executive and non-technical audiences.
Disclaimer
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