Data Scientist-Build Analytics

Forged Fiber 37•Chandler, AZ

About The Position

The Data Scientist – Build Analytics will support the end-to-end fiber network deployment lifecycle by leveraging data and analytics to improve build execution, forecasting, cost efficiency, and operational readiness. This role partners across Engineering, Construction, Field Operations, Supply Chain, and IT to deliver actionable insights that drive decision-making, reduce risk, and accelerate market readiness.

Requirements

Bachelor's or Master's degree in Data Science, Statistics, Engineering, Computer Science, or related field
3–7+ years of experience in data science, analytics, or related roles
Strong experience with Python (pandas, scikit-learn, etc.)
Strong experience with SQL and large dataset analysis
Strong experience with Data visualization tools (Power BI, Tableau, etc.)
Experience building predictive models and translating data into business insights
Strong problem-solving and analytical thinking skills
Ability to communicate complex findings to technical and non-technical audiences

Nice To Haves

Looker BI reporting
LLM training
AI/ML experience
Agentic AI development
Experience in telecom, fiber deployment, construction, or infrastructure programs
Familiarity with build lifecycle (planning → construction → readiness → launch)
Experience working with Vendor performance data
Experience working with Supply chain or logistics datasets
Experience working with Geospatial (GIS) data
Exposure to OSS/BSS systems or network deployment data

Responsibilities

Develop and maintain data models to track fiber build progress, productivity, and performance
Using statistical methods to analyze build lifecycle data (planning, permitting, construction, testing, readiness) to identify trends, patterns, risks and opportunities.
Establish KPIs and metrics aligned to build execution and market launch success
Build predictive models to forecast build timelines and completion rates, resource needs (labor, materials, contractors), and risk areas (permitting delays, vendor performance, supply constraints).
Support scenario modeling for market launches and expansion planning
Identify inefficiencies across construction, permitting, and deployment workflows
Provide data-driven recommendations to improve cycle times, cost per passing / cost per market, and resource utilization
Partner with build leadership to drive continuous improvement initiatives
Translate complex data into clear, actionable insights for leadership
Develop dashboards and visualizations showing build progress vs. plan, risk areas and mitigation tracking, and market readiness indicators
Support creation of executive-level summaries and ELT reporting
Integrate data across systems and domains: Construction & vendor systems, supply chain / materials tracking, Field operations & dispatch, OSS/BSS and network readiness
Ensure data consistency, quality, and accessibility across teams
Design and implement automated data pipelines and reporting solutions
Enable self-service analytics for build and operations teams
Partner with IT/Data Engineering to scale data infrastructure