Scientific Data Analyst
About The Position
This is a health research role, not business intelligence, financial analytics or management consulting. We are looking for someone who comes from life sciences research and is at home in study design, biostatistics and the messy reality of participant data. As a Science Data Analyst at Lasso, you sit inside a multidisciplinary research team and help answer real scientific questions. You shape analytical approaches, test them against study design, and turn tangled multi-modal data into findings that hold up. You are comfortable working alongside and communicating with people from every corner of the company, from researchers and software engineers to project managers.
Requirements
- A PhD in health or life sciences (for example epidemiology, public health, neuroscience, genetics, genomics, behavioral science or clinical and biomedical sciences). This one is firm: we want a researcher who analyzes data, not a data professional new to health research.
- 5+ years of full-time experience in data analysis and data science fundamentals (algorithms, data structures, data visualization), preferably in a clinical or research setting.
- 5+ years of full-time experience in a scientific programming language such as Python, R or Matlab, with hands-on familiarity with related frameworks and libraries (Tidyverse, SciPy, scikit-learn, 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.
- 1+ year working in a Linux environment, using version control (e.g., GitHub) and software virtualization platforms (e.g., Docker).
- Proficiency creating visualizations of complex longitudinal datasets.
- 3+ years working with data privacy regulations (HIPAA, GDPR) and research ethics standards.
Nice To Haves
- Background in neuroscience, genomics, clinical research or a related life sciences discipline.
- Experience with large-scale, multi-modal research datasets spanning imaging, EEG, eye-tracking, wearables, behavioral, genetic or biosample data.
- Familiarity with reproducible research practices including R Markdown, Quarto or equivalent notebook-based workflows.
Responsibilities
- Work directly with researchers to define data requirements, resolve quality issues and support reproducible research workflows.
- Conduct statistical analyses in R, Python and other tools, applying biostatistical methods suited to the study design, including mixed-effects models and longitudinal techniques.
- Evaluate and contribute to experimental and study design decisions, making sure analyses are valid and that confounds, covariates and sampling are handled properly.
- Apply statistical and computational methods for pattern detection, predictive modeling and data quality assessment across multi-modal datasets.
- Organize and manage datasets and data dictionaries, run QC checks and conduct preliminary analyses that keep data integrity high.
- Design, configure and maintain dashboards for domain-specific workgroups.
- Produce clear documentation of your methods, pipelines and findings for scientific and internal audiences.
- Maintain project tracking and help build a culture of rigorous, reproducible science.
Benefits
- Competitive compensation and benefits.
- A flexible, hybrid or remote position.
- Work that matters, with scientific and clinical impact you can point to.
- Variety that keeps it interesting: you work the whole analytics stack, from study design to insight, so no two projects feel the same.
- Room to grow with a mission-driven company at the front of research data management.
- A sharp, multidisciplinary team that is good at what it does and good to work with.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
