Senior Data Analyst

LifeLens Technologies Inc.•Warminster Township, PA

14d•Onsite

About The Position

LifeLens Technologies, Inc. is a medical device developer located in Ivyland, Pennsylvania, developing next-generation treatment devices for personal health monitoring. The Senior Data Analyst leads the analytical direction of retrospective data analysis and partners with Data Engineering to turn raw telemetry into refined, focused data narratives. The role builds and maintains the tools and foundational processes that help company stakeholders, technical teams, and end users understand what the data means and how it can make a difference. The analyst leads work at the intersection of wearable sensor hardware and advanced analytics, developing documented analysis tools and notebooks on the data lake and training others to use them. The role defines requirements for and curates the physiological recordings metadata database, maintains data inventory and provenance across hardware generations, and owns end-to-end data products ranging from pathology visualizations to statistical reports. It also interprets multi-source, time- and location-tagged data, contributes to analytical requirements and pipelines, creates exemplary data products and proof-of-concept analysis methods, investigates anomalies throughout the hardware-to-cloud stack, and advances tooling for evaluating the Ascent system, including statistical models and machine-learning workflows. This is a full-time, on-site position. The role calls for a collaborative, detail-oriented, goal-driven problem solver with strong organizational, verbal, and written communication skills. The company describes the compensation as competitive and offers benefits and long-term growth opportunities.

Requirements

Master’s degree in computer science, computer engineering, or a related technical discipline, or 15+ years of experience in total (listed as preferred).
10+ years of experience in software development, including at least 7+ years working as a Data Analyst or Data Scientist.
Strong experience with Python, MATLAB, or an equivalent language for data transformation and post-processing.
Experience working with regulated data or coding and handling data in a sensitive medical or industrial environment, such as HIPAA, PII, or PHI contexts.
Experience with scientific data formats such as HDF5, MATLAB, or Parquet.
Demonstrated experience analyzing large, complex datasets at TB–PB scale.
Experience curating heterogeneous legacy datasets and defining metadata requirements.
Strong software engineering practices, including Git, code review, continuous integration, and rigorous testing and validation.
Proven track record of technical data analysis and presentation.
Ability to work independently and proactively across teams and stakeholders.
Demonstrated success in a results-oriented, fast-paced, dynamic work environment.
Collaborative mindset, passion for developing new technology, attention to detail, goal orientation, problem-solving ability, strong organizational skills, and excellent verbal and written communication skills.

Nice To Haves

Experience with JavaScript/TypeScript, Rust, R, C, or C++.
Data modeling experience.
Experience with graphic design, vector graphics tools such as Adobe Illustrator, video editing tools such as Adobe Premiere or After Effects, or 3D modeling tools such as Blender or Maya.
Experience with Geospatial Information Systems (GIS) and custom GIS visualizations.
Experience with physiological or other sensor time-series data, such as ECG, PPG, or accelerometry.

Responsibilities

Lead the analytical direction of retrospective data analysis and partner with Data Engineering to turn raw telemetry into clear data narratives.
Develop and maintain well-documented analysis tools and notebooks using the data lake, and train others to use them.
Analyze raw biosignals and interpret complex data from wearable sensor hardware.
Define requirements for and curate the physiological recordings metadata database with Data Engineering, ensuring accurate inventory and provenance across hardware generations.
Manage legacy and new data inventory and provenance to support downstream data products.
Own data products from concept through delivery, including pathology visualizations and statistical reports for engineers, executives, and end users.
Ingest and curate multi-source, time- and location-tagged data; define ontologies and analytical requirements with Data Engineering for supporting pipelines.
Create exemplary data products that set standards for organizational data analysis and presentation.
Collaborate with the Algorithm Development team on proof-of-concept analysis methods that may become internal or external products.
Investigate data anomalies across the hardware-to-cloud stack, identify likely root causes, and work with the responsible engineering teams on resolution.
Collaborate with engineers, product leads, and stakeholders to understand data needs and translate business needs into internal tooling.
Design and operate tools for evaluating the Ascent system, including statistical models and machine-learning workflows for automated anomaly detection and trend analysis.
Adapt pipelines and visualizations as the hardware and software ecosystem evolves.