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# Data Scientist-AI - GTM/Propensity to Buy Modeling.

- **Company:** Eliassen Group
- **Location:** Dover, DE, United States (Remote available)
- **Salary:** $166,400.0 - $187,200.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, ARM Architecture, BigQuery, Software as a Service, Cloud Database, Database Queries, Python (Programming Language), Machine Learning, Data Streaming, Usage Analysis, Jupyter Notebook, Sql Optimization, Large Language Models, Snowflake, Scikit Learn, Data Objects
- **Published:** July 24, 2026
- **Apply:** https://www.nexxt.com/job.asp?id=3331048836&tx=IT878TYI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Proven experience building and analyzing ML models such as classification, regression, and clustering using cloud platforms and Python.
 * Experience with Propensity to buy (P2B) models.
 * LLM, ML Model experience.
 * Strong SQL skills with complex queries across cloud data warehouses like Snowflake, Redshift, or BigQuery.
 * Experience with Jupyter Notebooks and ML libraries such as scikit-learn and pandas.
 * Background with SaaS GTM metrics including pipeline generation, SDR workflows, customer success, or product usage analysis.
 * Exceptional communication skills with both technical and business audiences.
 * Collaborative and proactive work style with comfort operating autonomously in a matrixed environment.
 * Comfort with ambiguity and shifting requirements while maintaining progress.

## Description

* Partner with sales, marketing, SDR, and customer success stakeholders to translate business questions into analytical objectives.
 * Proactively engage stakeholders to identify use cases, validate assumptions, and gather feedback on model outputs.
 * Build and analyze ML models for lead scoring and customer propensity using Snowflake Cortex or similar platforms.
 * Develop in Jupyter Notebooks and write advanced SQL to explore, profile, and validate data.
 * Enhance and operationalize existing models, including an in-progress propensity-to-buy prototype.
 * Assess data quality, identify coverage gaps, and independently profile unfamiliar datasets.
 * Create clear business and technical artifacts including data flows, archetypes, and validation findings.
 * Present findings in simple language and translate predictions into recommended business actions.
 * Document key use cases, data objects, and validation results for business consumption.
 * Operate with autonomy, navigate a matrixed organization, and maintain momentum without extensive direction.
 * Bias toward action and iteration to deliver production-quality contributions quickly.


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