About careerzynith – Transforming Health & Wellness Through Innovation

careerzynith is a leading, technology‑driven health services organization dedicated to delivering personalized, affordable, and high‑quality care to millions of people across the United States. With a heritage rooted in community pharmacy, retail health, and integrated health‑plan management, careerzynith has evolved into a comprehensive health ecosystem that includes retail locations, specialty pharmacy, telehealth, and a robust data‑analytics platform. Our mission—“Carry our heart to every moment of your health”—guides every decision, from the way we design digital solutions to the way we empower our employees to make a meaningful impact.

As a remote‑first employer, careerzynith embraces flexibility, diversity, and continuous learning. We believe that a thriving workforce fuels innovative solutions, and we invest heavily in the professional growth, well‑being, and inclusion of every team member.

Position Overview

careerzynith is seeking a detail‑oriented

Data Entry Analyst – Healthcare Data Solutions

to join our Research & Development (R&D) and Analytics team. In this role, you will design, build, and maintain data pipelines, analytical models, and reporting tools that enable the organization to improve operational efficiency, enhance member experiences, and drive strategic decision‑making across our health‑care portfolio.

This is a fully remote, online position offering a competitive hourly rate of $26, with the flexibility to work from anywhere in the United States.

Key Responsibilities

Data Pipeline Development:

Design, implement, and optimize automated data extraction, transformation, and loading (ETL) processes that ingest large volumes of structured and unstructured health‑care data from multiple sources.

Analytical Modeling:

Build predictive and prescriptive models using statistical techniques and machine learning algorithms to identify trends, forecast outcomes, and recommend actionable solutions.

Process Improvement:

Identify opportunities to streamline claims processing, payment reconciliation, and member enrollment workflows, delivering measurable gains in speed and accuracy.

Data Quality Assurance:

Conduct rigorous data validation, cleansing, and integrity checks to ensure the reliability of analytics and reporting outputs.

Visualization & Reporting:

Create intuitive dashboards and visualizations using tools such as Tableau, Power BI, or Looker to communicate insights to cross‑functional stakeholders.

Collaboration:

Partner with product managers, engineers, clinicians, and business analysts to translate complex business problems into data‑driven solutions.

Documentation:

Maintain comprehensive documentation of data schemas, pipeline architectures, and analytical methodologies for knowledge sharing and regulatory compliance.

Continuous Learning:

Stay current with emerging data‑science techniques, health‑care regulations, and industry best practices to continuously enhance the analytical capabilities of the team.

Essential Qualifications

• Minimum

2 years of professional experience

in data analysis, data engineering, or a related quantitative field.

• Proficiency in programming languages such as

Python or R

for data manipulation, statistical analysis, and model development.

• Strong command of

SQL

for querying relational databases and optimizing complex queries.

• Demonstrated experience working with large health‑care datasets, including claims, pharmacy, or member enrollment data.

• Ability to interpret and translate business requirements into technical specifications and analytical deliverables.

• Excellent written and verbal communication skills, with the ability to present technical concepts to non‑technical audiences.

• Self‑starter mindset with strong organizational skills and the ability to manage multiple priorities in a remote environment.

Preferred Qualifications

• Advanced degree (M.S. or Ph.D.) in Data Science, Statistics, Computer Science, Health Informatics, or a related discipline.

• Experience with cloud platforms (AWS, Azure, or Google Cloud) and modern data‑engineering tools such as Apache Spark, Airflow, or dbt.

• Familiarity with health‑care industry regulations (HIPAA, HITECH) and data‑privacy best practices.

• Knowledge of machine‑learning frameworks (scikit‑learn, TensorFlow, PyTorch) and experience deploying models into production.

• Prior exposure to telehealth, specialty pharmacy, or health‑plan operations.

Core