Senior Data Analyst
Role Overview
The Senior Data Analyst leads retrospective data analysis and turns raw wearable telemetry into data narratives and products.
This is a full-time, on-site position.
The role works across wearable sensor hardware, data engineering, algorithms, product, and stakeholder teams.
Key Skills for This Role
Full Job Posting
About LifeLens
LifeLens Technologies is a medical device developer located in Ivyland, Pennsylvania.
The company develops next-generation treatment devices for personal health monitoring.
Role overview
The Senior Data Analyst leads retrospective data analysis and turns raw wearable telemetry into data narratives and products.
This is a full-time, on-site position.
The role works across wearable sensor hardware, data engineering, algorithms, product, and stakeholder teams.
Responsibilities
- Develop documented analysis tools and notebooks on the data lake and train others to use them.
- Analyze raw biosignals and build analytical products, including visualizations and statistical reports.
- Define and curate physiological recordings metadata requirements, inventory, and provenance across hardware generations.
- Ingest and curate multi-source, time- and location-tagged data; define ontologies and analytical requirements.
- Develop proof-of-concept analysis methods and data products with the Algorithm Development team.
- Investigate anomalies across the hardware-to-cloud stack and partner with engineering teams on resolution.
- Build tools for evaluating the Ascent system, statistical models, and ML workflows for anomaly detection and trend analysis.
- Translate business needs into internal tooling and adapt pipelines and visualizations as the ecosystem evolves.
Qualifications and competencies
- Strong experience with Python or MATLAB (or equivalent) for data transformation and post-processing.
- Experience with regulated or sensitive medical/industrial data, including HIPAA, PII, or PHI contexts.
- Experience with scientific data formats including HDF5, MATLAB, and Parquet; analysis of large datasets at TB–PB scale.
- Experience curating heterogeneous legacy datasets and defining metadata requirements; data modeling is a plus.
- Strong engineering practices including Git, code review, CI, rigorous testing, and validation.
- Technical data analysis and presentation experience; independent work across teams in a fast-paced environment.
- Graphic design, vector graphics, video editing, or 3D modeling experience is listed among competencies.
- Geospatial experience is listed; the description ends after this item.
- JavaScript/TypeScript, Rust, R, or C/C++ are listed as plus skills.
Tools and technical areas
- The role uses analytical tools and notebooks, scientific data formats, statistical models, and machine learning workflows.
- The data includes wearable sensor signals and physiological recordings; work spans a hardware-to-cloud stack.
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