Job description

About Egen: Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. If this describes you, we want you on our team. Want to learn more about life at Egen? Check out these resources in addition to the job description. Meet Egen Life at Egen Culture and Values at Egen Career Development at Egen Benefits at Egen RESPONSIBILITIES Data Ingestion: Design and build the pipelines that bring data in for feature engineering and machine learning. Feature Engineering: Use your knowledge of healthcare data, statistics, and data science to create features with real predictive power. Feature Extraction and Selection: Use techniques like clustering, text analytics, and PCA to find the most useful signals while preserving the meaning in the original data, so models stay accurate and scale well. Exploratory Data Analysis: Explore the data to find patterns, trends, and relationships, and use what you learn to match features with the right modeling approach. Model Development: Experiment with statistical and machine learning methods to build, tune, and optimize predictive models that fit the use case. Deployment: Ship production code that follows engineering best practices. Documentation and Communication: Document your work and share your approach, insights, and solutions with technical and non-technical audiences. Monitoring: Build and maintain the pipelines and processes that keep our enterprise data science products running smoothly. Quality and Integrity: Keep data and outputs accurate and reliable from start to finish. Collaboration: Partner with stakeholders, data scientists, data engineers, and product managers to build and deliver great data products. QUALIFICATIONS Experience: 6+ years working across data science and machine learning (for the Lead level). Core Languages: Expert-level SQL and Python. Data Types: Hands-on work with both structured and unstructured data, such as tables, text, images, and video. Databases: Experience with both relational (SQL) and non-relational (NoSQL) databases. Data Processing: Familiarity with ETL and data processing tools like Apache Spark. Visualization and Monitoring: Experience using tools to visualize data and monitor its health. Engineering Practices: Comfort with modern software practices like automated testing and CI/CD. Containers: Experience with Docker. Cloud: Experience deploying to GCP or another cloud platform. BONUS EXPERIENCE Consulting or Professional Services Background Domain Knowledge of Healthcare Data Knowledge of Healthcare Protocols such as HL7, FHIR, DICOM Compensation & Benefits: This role is eligible for our competitive salary and comprehensive benefits package to support your well-being: - Comprehensive Health Insurance - Paid Leave (Vacation/PTO) - Paid Holidays - Sick Leave - Parental Leave - Bereavement Leave - 401 (k) Employer Match - Employee Referral Bonuses Check out our complete list of benefits here - > egen.ai Important: All roles are subject to standard hiring verification practices, which may include background checks, employment verification, and other relevant checks. EEO and Accommodations: Egen is an equal opportunity employer and is committed to inclusion, diversity, and equity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Egen will also consider qualified applications with criminal histories, consistent with legal requirements. Egen welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team if you require accommodations during the interview process.

Requirements

Must-haves

Data Ingestion: Design and build the pipelines that bring data in for feature engineering and machine learning.
Feature Engineering: Use your knowledge of healthcare data, statistics, and data science to create features with real predictive power.
Feature Extraction and Selection: Use techniques like clustering, text analytics, and PCA to find the most useful signals while preserving the meaning in the original data, so models stay accurate and scale well.
Exploratory Data Analysis: Explore the data to find patterns, trends, and relationships, and use what you learn to match features with the right modeling approach.
Model Development: Experiment with statistical and machine learning methods to build, tune, and optimize predictive models that fit the use case.
Deployment: Ship production code that follows engineering best practices.
Documentation and Communication: Document your work and share your approach, insights, and solutions with technical and non-technical audiences.
Monitoring: Build and maintain the pipelines and processes that keep our enterprise data science products running smoothly.
Quality and Integrity: Keep data and outputs accurate and reliable from start to finish.
Collaboration: Partner with stakeholders, data scientists, data engineers, and product managers to build and deliver great data products.
Experience: 6+ years working across data science and machine learning (for the Lead level).
Core Languages: Expert-level SQL and Python.
Data Types: Hands-on work with both structured and unstructured data, such as tables, text, images, and video.
Databases: Experience with both relational (SQL) and non-relational (NoSQL) databases.
Data Processing: Familiarity with ETL and data processing tools like Apache Spark.
Visualization and Monitoring: Experience using tools to visualize data and monitor its health.
Engineering Practices: Comfort with modern software practices like automated testing and CI/CD.
Containers: Experience with Docker.
Cloud: Experience deploying to GCP or another cloud platform.
Consulting or Professional Services Background
Domain Knowledge of Healthcare Data
Knowledge of Healthcare Protocols such as HL7, FHIR, DICOM

Nice-to-haves

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Application prep

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Keywords to mirror

machine learningsqlpythonci/cddockergcpai

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

Vision

Pto

Parental Leave