Lila Sciences, Inc. | Cambridge, MA Data Scientist - Materials

Flagship Pioneering; Cambridge (MA); On-site; USD 85,000 - 130,000; Full time

Job summary

Lila Sciences is seeking a Data Scientist to join their Physical Sciences organization. The ideal candidate will leverage their expertise in data analysis and machine learning to transform complex datasets into actionable insights, driving informed decisions in a cutting-edge autonomous lab environment. Collaborate with various scientific professionals, engage in innovative projects, and contribute to pioneering advancements in human health and sustainability. Candidates should possess a Master’s or Ph.D. and have experience in the physical sciences sector.

Qualifications

• 2+ years of experience in data analysis, statistical modeling, or machine learning.
• Proficiency in Python (pandas, NumPy) and SQL.
• Experience with ETL workflows and data visualization.

Responsibilities

• Design and maintain ETL pipelines for various data sources.
• Develop statistical or machine learning models to analyze performance metrics.
• Create dashboards and reports to present insights to teams.

Skills

Data Analysis

Statistical Modeling

Machine Learning

Data Visualization

Feature Engineering

ETL Development

Education

Master’s or Ph.D. in Data Science, Statistics, Materials Science, Chemistry, Physics

Tools

Python

SQL

ETL Tools (like Airflow)

Data Visualization Libraries (like Plotly)

Docker

AWS

Job description

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 introducingscientific superintelligence to solve humankind's greatestchallenges, 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.

As a Data Scientist in our Physical Sciences organization, you will transform complex experimental and testing datasets into actionable insights that drive our autonomous lab’s decision-making. You’ll partner with electrochemists, synthesis chemists, characterization specialists, and automation engineers to ensure data quality, build predictive models, and inform scientific campaigns across materials and device development.

• Data Infrastructure: Design and maintain robust ETL pipelines to ingest, validate, and preprocess data from diverse sources—electrochemical tests, materials characterization, and automated lab instruments.
• Feature Engineering & Modeling: Perform domain-relevant data transformations, extract meaningful descriptors from raw data (e.g., voltage curves, spectroscopic signatures, image-based measurements) and develop statistical or machine learning models to relate independent variables (time, composition, etc.) to performance metrics and failure modes.
• Analytics & Visualization: Create interactive dashboards and reports to communicate trends, anomalies, and key insights to scientific and engineering teams.
• Active Learning Support: Collaborate with ML scientists to integrate your analytical outputs into active learning loops, helping to prioritize experiments and optimize resource allocation.
• Cross-Functional Partnership: Work closely with R&D leadership, Product Managers, and automation specialists to translate scientific questions into data requirements and modeling strategies.
• Reproducibility & Documentation: Establish best practices for code versioning, data provenance, and analysis notebooks; contribute to internal knowledge bases and publications.

What You’ll Need to Succeed

• Master’s or Ph.D. in Data Science, Statistics, Materials Science, Chemistry, Physics, or a related quantitative field.
• 2+ years of experience in data analysis, statistical modeling, or machine learning—ideally applied to physical sciences or engineering datasets.
• Proficiency in Python (pandas, NumPy, scikit-learn) and SQL for data manipulation and analysis.
• Hands-on experience building ETL workflows using tools like Airflow, Prefect, or similar.
• Strong foundation in experimental design, statistical inference, and multivariate analysis.
• Familiarity with data visualization libraries (Plotly, Dash, or similar) and dashboard frameworks.
• Experience working with electrochemical or materials characterization data (e.g., impedance spectroscopy, X-ray diffraction, electron microscopy).
• Materials-specific python libraries (pymatgen)
• Exposure to cloud-based data platforms (AWS, GCP, or Azure) and scalable storage solutions.
• Knowledge of containerization (Docker, Singularity) and workflow orchestration (Snakemake, Nextflow).
• Prior contributions to open-source data tools or scientific software.
• Understanding of active learning, Bayesian optimization, or uncertainty quantification in experimental contexts.

Lila Sciences iscommitted 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.

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