In this vital role, you will be a Data Scientist responsible for developing and deploying advanced machine learning, operational research, and statistical methods to uncover insights from large datasets. This position involves creating analytics solutions to address customer needs within the US Value & Access domain. You will work with a global, cross-functional team to develop, deploy, and apply predictive and prescriptive analytics, particularly focusing on AI-driven automation and insights.
Roles & Responsibilities
- Model Development & Deployment: Develop and deploy advanced machine learning, semantic analysis, and statistical models. You will be responsible for managing a proprietary AI engine, ensuring models are trained with the latest data and meet SLA expectations.
- Data Analysis & Modeling: Utilize technical skills like hypothesis testing, machine learning, and retrieval processes to identify trends and analyze relevant information. You will perform exploratory and targeted data analyses and manage the model/analytics experiment and development pipeline leveraging MLOps.
- AI & Automation: Collaborate with technical teams to translate business needs into technical specifications, particularly for AI-driven automation and insights. You will develop and integrate custom applications and intelligent dashboards that incorporate AI capabilities to enhance decision-making and efficiency.
- Collaboration & Expertise: Work with a global, cross-functional team on the AI tool's roadmap. You will act as a subject matter expert, solving development and commercial questions and staying updated with industry trends and technologies.
Qualifications
- A Master's degree with 1-3 years of experience, a Bachelor's degree with 3-5 years of experience, or a Diploma with 7-9 years of experience in computer science, statistics, or other STEM majors.
- Experience with one or more analytic tools or languages like R and Python.
- A foundational understanding of the US pharmaceutical ecosystem, patient support services, and standard datasets.
- Strong foundation in machine learning algorithms and techniques, including regression, clustering, and classification.
- Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow) and DevOps tools (e.g., Docker, Kubernetes, CI/CD) is a plus.
- Any AWS Developer, Python, or ML certification is preferred.
Soft Skills
- Problem-Solving: Outstanding analytical and problem-solving skills with the initiative to explore alternate technologies and approaches.
- Communication: Excellent communication and interpersonal skills to collaborate with global, virtual teams and translate business needs into technical specifications.
- Initiative: A high degree of initiative and self-motivation with the ability to learn quickly and manage multiple priorities successfully.
