Data Scientist II

Job Title

Data Scientist II - Commercialization EF DS AI Solutions

Job Summary

The Data Scientist II role is a high-impact idea generator and deep problem solver within analytics team. This role is for individuals who can challenge assumptions, conceptualize novel analytical approaches, and harness emerging technologies to deliver transformative business solutions. This role is designed for an experienced data scientist with strong time-series forecasting expertise who can independently own end-to-end forecasting workstreams, improve and scale enterprise forecasting platforms, and lead initiatives that directly influence commercial and strategic decision-making.

This individual operates with a high degree of autonomy, translating ambiguous business problems into robust forecasting solutions, partnering closely with Finance, Commercial, Market Access, and Analytics stakeholders. The role blends deep technical execution, platform thinking, and stakeholder leadership

Responsibilities

• Ideate, architect, and prototype high-impact analytical and ML/AI solutions.
• Break down ambiguous business problems into clear, scalable analytical workflows.
• Design, develop, and maintain advanced forecasting models using statistical, machine learning, and AI-driven approaches across commercial demand and planning use cases.
• Partner with stakeholders to frame hypotheses, define KPIs, and design experiment frameworks.
• Explore and analyse large structured and unstructured datasets from clinical, digital, and real-world sources.
• Apply advanced ML, statistical methods, and LLM-based techniques to generate business solutions.
• Work with engineering teams to ensure reliable data pipelines and deployment environments.
• Present complex model logic and recommendations clearly to technical and non-technical audiences.
• Continuously evaluate and adopt emerging AI/ML and agentic-system tools to future-proof capabilities.
• Mentor junior data scientists and promote a culture of experimentation and innovation.
• Apply MLOps best practices including version control, reproducibility, model monitoring, and documentation.

Experience

• Bachelor's, Master's, or PhD in Data Science, Computer Science, Statistics, Engineering, or related field.
• 2-5 years of experience in applied ML and problem-solving roles.
• Experience working with large structured/unstructured datasets (SQL, NoSQL, document stores).
• Demonstrated success building predictive or AI models with measurable business impact.
• Experience with healthcare, clinical trials, or regulated pharma datasets (preferred).
• Strong hold on MLOps tools: MLflow, Git, CI/CD, containerization.

Skills and Competencies

• Strong hands-on expertise in Python-based data science and analytical workflows.
• Strong foundations in statistics, hypothesis testing, and experimental design.
• Excellent communication, storytelling, and stakeholder-influence abilities.
• Solid experience in time-series forecasting (ARIMA, Prophet, Holt-Winters); multivariate and hierarchical forecasting is a plus.
• Experience with AWS/Azure/GCP and modern data ecosystems (Spark, Databricks, BigQuery, Snowflake).
• Comfortable navigating fast-paced, ambiguous problem environments with autonomy.
• Strong foundations in experimentation, causal inference, and hypothesis-driven analysis.
• Advanced visualization skills using Tableau, Power BI, matplotlib, seaborn, or Plotly.
• Solid understanding of agentic AI architectures and autonomous or semi-autonomous analytical agents.

Good to Have

• Experience enhancing or scaling enterprise level platforms.
• Exposure to AI-augmented or LLM-assisted analytics workflows applied to planning or forecasting use cases.
• Ability to prototype autonomous agents that handle multi-step or decision-making workflows.
• Familiarity with tool-calling, orchestration, and autonomous reasoning toolkits.
• Contributions to open-source AI/LLM projects or participation in relevant hackathons.

On-site Protocol

BMS has an occupancy structure that determines where an employee is required to conduct their work. This structure includes site-essential, site-by-design, field-based and remote-by-design jobs. The occupancy type that you are assigned is determined by the nature and responsibilities of your role.