The Efficient Growth team within USRM Data Science (USDS) is focused on understanding the economics of lead sources, distribution channels and products to drive optimal business decisions. In this role, you will be on a small but critical team responsible for developing lifetime value models and datasets for marketing analysis and growth decisions. This role will require strong technical and analytical capabilities.
Focuses on data integration, engineering, modeling, and business intelligence, with an emphasis on transforming legacy systems into a cloud-native stack using Databricks, dbt, and other cloud platforms. Plays a key role in developing and maintaining scalable, performance-optimized BI solutions and data pipelines, enabling self-service analytics and driving actionable insights across the organization. Ensures the quality and reliability of data.
As a Staff AI Researcher, you will develop ML and AI solutions that will improve health for millions of people. You will partner with other engineering and analytics teams, bringing AI technology into existing products and workflows. As a Staff AI Researcher, you will lead the way to harness knowledge from one of the most extensive data sets of medical records, diagnoses, claims, and prescriptions.
N-Power Medicine is hiring a Senior Clinical Data Scientist to support clinical trial analysis and reporting by developing, validating, and maintaining SAS and/or R programs. Responsibilities include extracting and transforming clinical data, creating and verifying analysis datasets, and delivering both interim and final datasets to pharmaceutical partners. You will support clinical study teams in programming data for review, analysis and presentation.
Lead and grow our data engineering team, overseeing the implementation, integration, and maintenance of client data into SmarterDx products and services. Accountable for team performance including feature delivery, quality, operational excellence, hiring, retention, professional growth, and well-being. Guide the design and implementation of robust ETL pipelines and drive continuous improvement in data processes.
Advance clinical AI by building scalable pipelines and infrastructure for data ingestion, processing, and analysis. Integrate and standardize diverse healthcare data (clinical, coding, billing) and design resilient, fault-tolerant frameworks and data models leveraging AWS (S3, Lambda, Step Functions, Batch). This role spans the full data lifecycle, shaping the foundation for next-generation clinical AI solutions.
Seeking an experienced Data Engineer to assist with the migration of our Energy Portfolio data from a legacy application to Gentrack Junifer. This contract role involves designing and building scalable data pipelines, data modeling, and collaborating with stakeholders to build analytics solutions. The role delivers progress by improving data and reducing complexity.
This role is ideal for a technical expert with deep experience in Oracle SQL/PLSQL and Java, combined with a strong background in HL7 messaging and healthcare data exchange. The ideal candidate will be a self-driven engineer who thrives in a fast-paced, collaborative environment and can clearly communicate complex data issues to both technical and non-technical stakeholders. This position will be fully remote.
The Deep Learning Analytics Center of Excellence at General Dynamics Mission Systems is seeking a seasoned data scientist with experience in Deep Learning, data visualization, software development, and full Machine Learning life cycle management. This role interfaces with business development teams, identifies AI/ML initiatives, engages with clients, and leads interdisciplinary teams in tackling DL challenges.
Shape the future of AI as a mathematics expert fluent in Spanish (Latin America). You will challenge advanced language models on topics like real and complex analysis, optimization theory, mathematical proofs, set theory, probability distributions, cryptographic algorithms, and applied mathematics—documenting every failure mode so we can harden model reasoning. You will converse with the model on classroom problems and theoretical mathematics questions, verify factual accuracy and logical soundness, capture reproducible error traces, and suggest improvements to our prompt engineering and evaluation metrics.