Senior Data Scientist – Applied AI
| Company | Flagship Pioneering |
|---|---|
| Location | Cambridge, MA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Senior |
Requirements
- Ph.D. or Master’s degree in a scientific field of study.
- 3+ years of industry experience in data science, analytics, or ML model development—ideally in a production environment.
- Strong Python skills with a solid grasp of object-oriented programming principles.
- Hands-on experience in machine learning, data analysis, and statistical modeling.
- Familiarity with natural language processing techniques, especially for text data analytics and model evaluation.
- Proven ability to transform raw data into actionable insights using modern data analysis libraries (e.g., Pandas, Plotly, or similar).
- Exceptional communication skills with the ability to distill complex technical concepts for stakeholders across disciplines.
Responsibilities
- Gather and preprocess large volumes of internal chat logs, applying statistical methods and NLP techniques to uncover trends, patterns, and areas for LLM improvements.
- Design and implement experiments to assess model performance, guiding model tuning and feature enhancements based on empirical evidence.
- Work alongside Data Engineers and ML researchers to build robust data pipelines; translate insights into data-driven recommendations for stakeholders across the organization.
- Develop dashboards and visualizations that effectively communicate complex findings to both technical and non-technical audiences, facilitating informed decision-making.
- Apply machine learning techniques to generate predictive insights, explore generative AI methods, and validate data-driven hypotheses.
- Champion best practices in reproducible research, version control, and documentation to ensure reliability and scalability of data workflows.
Preferred Qualifications
- Prior work on data pipelines specifically supporting ML or generative AI models; familiarity with the MLOps lifecycle.
- Hands-on experience with vector database and RAG techniques for AI systems.
- Exposure to or experience building agent-driven platforms where AI systems autonomously execute complex tasks.
- Comfort with container orchestration and scaling using Kubernetes.
- Experience adapting quickly and delivering results in a fast-paced, evolving environment.
- Exposure to life sciences, material sciences, or related fields.