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# Data Scientist with ML

- **Company:** Spirite Industries, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Big Data, Software Quality, Extract Transform Load (ETL), Data Visualization, Database Queries, Python (Programming Language), Machine Learning, Marketing Information Systems, NumPy, SQL Databases, Model Validation, Pandas, Scikit Learn, Statistics Packages
- **Published:** July 21, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/p61novwy0b

## About the Role

* Minimum 5 years of experience as a Data Scientist
 * Strong proficiency in SQL - ability to write complex queries, join large datasets, and prepare data for modeling
 * Proficiency in Python (libraries such as pandas, numpy, scikit-learn, statsmodels or similar) - focus on data analysis, modeling, and result validation
 * Experience working with large datasets and ETL processes
 * Ability to work with business data and interpret analytical results
 * Attention to code quality and reproducibility of analyses
 * Ability to communicate effectively in English in an international environment

Nice to have

 * Expertise in the marketing and consumer analytics domain
 * Good understanding of segmentation models (clustering, scoring, customer profiling) and predictive models (propensity, churn, LTV)
 * Basic knowledge of model validation and performance metrics (AUC, lift, confusion matrix)

## Description

* Build and optimize predictive models (e.g., churn, propensity, segmentation, CLV, recommendations)
 * Analyze consumer and marketing data across multiple sources
 * Model and optimize customer journeys using AI/ML techniques
 * Collaborate with marketing and product teams to define analytical priorities
 * Translate complex analytical findings into clear business recommendations
 * Deliver analytical outputs together with actionable usage guidance for business teams
 * Prepare high-quality presentations, reports and data visualizations for non-technical stakeholders
 * Track model performance and measure business impact on marketing KPIs and P&L
 * Identify new opportunities for data-driven growth and champion data-based decision-making across the organization


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