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# REMOTE- Data Scientist

- **Company:** Insight Global
- **Location:** Manchester, CT, United States
- **Salary:** $166,400.0 - $176,800.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, DevOps, Digital Assets, Python (Programming Language), Operational Data Store, SQL Databases, Application Enhancement Tool, Feature Engineering, Snowflake, Data Pipelines, Siop
- **Published:** August 5, 2026
- **Apply:** https://dejobs.org/x/x/4959DB99872C4578A1D1640F7088332D/job/

## About the Role

1) SQL, Python, building and managing data pipelines in Snowflake, everything related to dev Ops

2) Modeling including data prep (join different tables, create cuts, create longitudinal data sets etc.), feature engineering (create calculated metrics for complex analysis), and forecasting and so on

3) Independent thinking to quality check output; ask questions; and generate insights

## Description

The Lead Data Scientist will build data, analytics, and AI solutions that support Sales, Inventory, and Operations Planning (SIOP) transformation and drive business impact across inventory performance and customer service. This individual will partner closely with business stakeholders to transform complex operational data into actionable insights, scalable analytics products, forecasting models, dashboards, and AI-powered tools., Deliver high-impact analytics by partnering with cross-functional leaders to define business problems and develop solutions that improve inventory, on-time delivery, and cost performance.

Build end-to-end analytics products, including:

Data pipelines and Snowflake tables

Forecasting and time-series models

Inventory and clear-to-build analytics

Dashboards and reporting assets

AI-powered decision-support tools

Maintain and operate analytics assets in production, ensuring accuracy, reliability, and ongoing performance.

Engineer trusted datasets by transforming large, complex operational data into structured, high-quality data assets and implementing continuous improvement frameworks for data quality.

Translate analytical findings into clear recommendations and drive adoption of tools and insights across the organization.


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