Senior Data Scientist, Applied AI

🚀 About Lila Sciences

Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai    

At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, please apply.

🌟 Your Impact at Lila

We are seeking a  Senior Data Scientist to join our Applied AI group and lead data-driven initiatives that enhance our Large Language Model (LLM) capabilities and advance our mission toward Scientific Superintelligence. In this role, your primary focus will be collecting, monitoring, and analyzing chat logs to uncover actionable insights for continuous LLM refinement. You will collaborate closely with software engineers, ML researchers, and domain scientists to design analytical workflows, evaluate model performance in real-world settings, and help instill best practices for data-centric decision-making in AI. 

🛠️ What You'll Be Building

• Data Collection & Analysis: Gather and preprocess large volumes of internal chat logs, applying statistical methods and NLP techniques to uncover trends, patterns, and areas for LLM improvements. 
• LLM Evaluation & Optimization: Design and implement experiments to assess model performance, guiding model tuning and feature enhancements based on empirical evidence. 
• Cross-Functional Collaboration: Work alongside Data Engineers and ML researchers to build robust data pipelines; translate insights into data-driven recommendations for stakeholders across the organization. 
• Data Visualization & Reporting: Develop dashboards and visualizations that effectively communicate complex findings to both technical and non-technical audiences, facilitating informed decision-making. 
• Statistical Modeling & ML: Apply machine learning techniques to generate predictive insights, explore generative AI methods, and validate data-driven hypotheses. 
• Continuous Improvement: Champion best practices in reproducible research, version control, and documentation to ensure reliability and scalability of data workflows. 

🛠️ What You'll Be Building

• Educational Background: Ph.D. or Master’s degree in a scientific field of study. 
• Professional Experience: 3+ years of industry experience in data science, analytics, or ML model development—ideally in a production environment. 
• Technical Proficiency: 
  • Python & OOP: Strong Python skills with a solid grasp of object-oriented programming principles. 
  • ML & Statistical Methods: Hands-on experience in machine learning, data analysis, and statistical modeling. 
  • NLP: Familiarity with natural language processing techniques, especially for text data analytics and model evaluation. 
  • Data Analysis & Visualization: Proven ability to transform raw data into actionable insights using modern data analysis libraries (e.g., Pandas, Plotly, or similar). 
• Communication & Collaboration: Exceptional communication skills with the ability to distill complex technical concepts for stakeholders across disciplines. 

✨ Bonus Points For

• Experience with ML & Generative AI: Prior work on data pipelines specifically supporting ML or generative AI models; familiarity with the MLOps lifecycle. 
• Retrieval-Augmented Generation (RAG): Hands-on experience with vector database and RAG techniques for AI systems. 
• Agentic AI Systems: Exposure to or experience building agent-driven platforms where AI systems autonomously execute complex tasks. 
• Kubernetes Proficiency: Comfort with container orchestration and scaling using Kubernetes. 
• Startup Environment: Experience adapting quickly and delivering results in a fast-paced, evolving environment. 
• Domain Background: Exposure to life sciences, material sciences, or related fields. 

🌈 We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

🤝 A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.