Data Analyst

Data Analyst/Scientist

Lasso Informatics is a SaaS start-up with a live research data management and analysis platform that brings together multi-modal (imaging, genetics, behavioral, and biosample) data for large-scale studies. Thousands of researchers across the globe rely on our platform today, and we are rapidly iterating and improving to push the boundaries of what is possible in research data management.

We live to innovate, and empower scientists to focus on the science, not the technology, leading to a faster time to science and cure.

Our team is incredibly diverse both by background and expertise, and that is not by accident. We believe that the most creative and powerful solutions come from different ways of thinking about the world. You will be working in an inspiring ecosystem alongside world-renowned professionals in medicine, physics, engineering, imaging, epidemiology, software development, and genetics. We thrive on empowering our colleagues to be thought leaders and innovate fresh new solutions for an exciting and rapidly changing field.

Key Responsibilities

• Design, configure, and maintain dashboards for domain specific workgroups.
• Conduct statistical analyses using R, Python, and other programs, applying biostatistical methods appropriate to study design - including mixed-effects models and longitudinal data techniques.
• Apply AI and machine learning tools to support pattern detection, predictive modeling, and data quality assessment across multi-modal datasets.
• Evaluate and contribute to experimental and study design decisions, ensuring analytical validity and appropriate handling of confounds, covariates, and sample considerations.
• Organize and manage datasets and data dictionaries, perform QC checks and conduct preliminary analyses to maintain high data integrity.
• Collaborate cross-functionally to define data requirements, resolve quality issues, and support reproducible research workflows.
• Produce clear technical documentation of analytical methods, pipelines, and findings for both scientific and internal audiences.
• Maintain project tracking and contribute to a culture of rigorous, reproducible science.

Qualifications

• PhD in Neuroscience, Psychology, Epidemiology, Engineering, Statistics, Computer Science, or an equivalent combination of education and experience.
• 5+ years of full time experience in data analysis and data science fundamentals - algorithms, data structures, data visualization, and machine learning - preferably in a clinical or research setting.
• 5+ years of full time experience in scientific programming language such as Python, R, or Matlab, with hands-on familiarity with related frameworks and libraries (e.g., Tidyverse, Scipy, Sklearn, PyTorch, Polars).
• Advanced proficiency in biostatistics, including methods suited to observational (cross-sectional and longitudinal) and experimental research designs.
• Demonstrated experience applying experimental design principles: power analysis, randomization, control conditions, and bias mitigation.
• Demonstrated experience leveraging AI/ML tools in an analytical or research context.
• 1+ year of experience working in a Linux environment, using version control systems (e.g., GitHub), and software virtualization platforms (e.g., Docker).
• Proficiency in creation of visualizations of complex longitudinal data sets
• 3+ years experience working with data privacy regulations (HIPAA, GDPR) and research ethics standards.

Preferred

• Background in neuroscience, genomics, clinical research, or a related life sciences discipline.
• Experience with large-scale, multi-modal research datasets spanning imaging, behavioral, genetic, or biosample data.
• Familiarity with reproducible research practices including R Markdown, Quarto, or equivalent notebook-based workflows.

Skills and Attributes

• Analytical rigor combined with scientific curiosity
• Self-directed and comfortable working autonomously in a distributed remote environment, managing multiple priorities and deadlines effectively.
• Strong technical writing skills with a demonstrated ability to document methods and communicate findings with precision.
• Excellent verbal communication skills; able to translate complex statistical findings for non-technical stakeholders.
• Solid understanding of data privacy obligations and a commitment to responsible data stewardship.
• High attention to detail and a genuine commitment to data integrity and reproducibility.
• Collaborative by nature, with strong interpersonal skills and comfort contributing to open, multidisciplinary team environments.

Benefits

• Flexible hybrid/remote position.
• Competitive full-time compensation and benefits package.
• Meaningful contribution to research with real-world scientific and clinical impact.
• Hands-on experience across the full analytics stack - from experimental design through insight delivery.
• Opportunity to grow with an innovative, mission-driven company at the forefront of research data management.
• Collaborative environment within a diverse, multidisciplinary team of researchers and data professionals.