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# Staff Data Scientist | Analytics

- **Company:** Machinify, Inc.
- **Location:** La Grange, KY, United States
- **Experience:** Experienced
- **Salary:** $180,000.0 - $240,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Data Analysis, Big Data, BigQuery, Data Files, Data Visualization, Power BI, SQL Databases, Tableau (Software), Usage Analysis, Large Language Models, Snowflake, Stream Analytics, Looker Analytics, GPT, Data Pipelines, Databricks
- **Published:** July 14, 2026
- **Apply:** https://www.juju.com/job/00000000ggi2yl

## About the Role

High School Diploma/G.E.D., 4-6 years of experience in product analytics OR a high-level business analytics role (quantifying product/business impact, translating data/insights/KPI into business decisions, showing impact using data and analysis, translating complex data analysis into stories to influence business decisions, etc.)

Strong proficiency in SQL and Python for data manipulation, analysis, and visualization

Demonstrated ability to independently diagnose complex business problems through exploratory data analysis - comfortable working without predefined solutions

Experience with BI/visualization tools (e.g., Power BI, Tableau, Looker) and building dashboards for business stakeholders

Familiarity with LLMs and AI tools for data analysis - comfortable using AI-assisted workflows to accelerate insight generation and excited to push the boundaries of what's possible

Degree in a quantitative field (e.g., Economics, Statistics, Operations Research, Data Science, Engineering, or related discipline)

Strong communication and storytelling skills - ability to work directly with senior stakeholders and present complex findings clearly to both technical and non-technical audiences

Experience working with large-scale data warehouses (Databricks, Snowflake, BigQuery, or similar)

Consulting mindset: ability to quickly build business context in unfamiliar domains, structure ambiguous problems, and deliver actionable recommendations

Curiosity, growth mindset, and excitement about learning new areas - including healthcare, LLMs, and GenAI

Comfort operating in a fast-paced, ambiguous environment and balancing short-term deliverables with longer-term strategic work

Nice-to-Haves

Background in management consulting or strategic advisory (McKinsey, BCG, Bain, Deloitte, or similar)

Economics background with understanding of causal reasoning and business impact measurement

Experience in healthcare, payment integrity, or health insurance operations (or strong aptitude for quickly learning complex regulated domains)

Experience with financial modeling and simulation - quantifying the impact of operational changes

Familiarity with event-stream analytics and user behavior analysis

Experience with dbt, Airflow, or similar data pipeline tools

## Description

Independently partner with business stakeholders to understand complex operational challenges, build domain context, and frame analytical approaches - without predefined solutions or heavy direction

Perform deep exploratory data analysis across payment operations, chart review workflows, and customer performance metrics to diagnose root causes and identify optimization opportunities

Build and maintain dashboards and reporting infrastructure that give leaders real-time visibility into operational and financial performance

Conduct business reviews including month-to-month bridges, waterfall analyses, and price-volume-mix decompositions to explain performance variance

Use LLMs and AI-assisted tools (e.g., Claude, GPT) for data exploration, pattern identification, and accelerating analytical workflows

Translate ambiguous business questions into structured analytical approaches with clear, actionable recommendations

Work with both models and querying to understand problems - diagnosing first, then standardizing and building scalable solutions

Partner cross-functionally with Product, Data Science, Engineering, and Operations teams to deliver analytics-ready solutions

Build foundational data assets - base data sets, aggregates, and pipelines - required for robust reporting and insight generation


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