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DATA SCIENTIST LEAD

Talent Groups

Seattle, Washington • Today

Easy Apply

Contract, Third Party

60 - 65

Data Scientist Lead

Talent Groups

Seattle, Washington • Today

Easy Apply

Contract, Third Party

55 - 65

Sr. Data Scientist

QUANTUM TECHNOLOGIES LLC

Remote • 7d ago

Easy Apply

Contract

85 - 90

Senior Data Scientist

QUANTUM TECHNOLOGIES LLC

Remote • 12d ago

Easy Apply

Third Party, Contract

80 - 90

Job Details

Skills

PySpark
Python
Statistics
Predictive Modelling
Performance Tuning
Snowflake
Git

Summary

What they’ll do

Validate the score against customer outcomes such as churn and care contacts
Build and optimize large-scale data pipelines in Databricks
Catch and fix data quality issues before results reach leadership
Document methodology, data lineage, and logic changes
Explain findings to non-technical and executive audiences

Must-haves:

Python/PySpark and advanced SQL
Databricks performance tuning (partitioning, Delta optimization, cluster sizing)
Applied statistics, especially predictive modeling for yes/no outcomes like churn
Git/version control and production-quality coding habits
Habit of checking whether results make sense as they work; skeptical of results that look too good
Strong documentation and organizational discipline

Strong preferences

Weight of Evidence/Information Value (WoE/IV) experience
Wireless or telecom network KPI knowledge
Snowflake experience
Experience building composite scores, indices, or scorecards

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 91099677
Position Id: 9137559
Posted 4 hours ago

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Contact the job poster

Srinath Deekonda

Delivery Manager - Recruitment @ Talent Groups

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Job Details

Skills

PySpark
Python
Statistics
Predictive Modelling
Performance Tuning
Snowflake
Git

Summary

What they’ll do

Validate the score against customer outcomes such as churn and care contacts
Build and optimize large-scale data pipelines in Databricks
Catch and fix data quality issues before results reach leadership
Document methodology, data lineage, and logic changes
Explain findings to non-technical and executive audiences

Must-haves:

Python/PySpark and advanced SQL
Databricks performance tuning (partitioning, Delta optimization, cluster sizing)
Applied statistics, especially predictive modeling for yes/no outcomes like churn
Git/version control and production-quality coding habits
Habit of checking whether results make sense as they work; skeptical of results that look too good
Strong documentation and organizational discipline

Strong preferences