Director Operations Analytics

Republic Services•Phoenix, AZ

2d•Hybrid

About The Position

The Director of Operations Analytics leads the Data Science, Analytics Engineering, and Business Intelligence teams supporting Collection Operations, Safety, Fleet, and Environmental Solutions. The incumbent is responsible for defining and executing the operations analytics roadmap, including volume forecasting, staffing, route planning and execution, and driver and supervisor coaching through production models, curated data products, and reporting solutions used by field and area leaders. The Director of Operations Analytics drives logistics and route optimization initiatives while overseeing a portfolio of business intelligence and analytics products serving more than 3,000 monthly active users and establishing the standards, capabilities, and data foundations required to scale enterprise analytics. The incumbent leads teams of data scientists, GIS and machine learning engineers, analytics engineers, data analysts, BI developers, and quality assurance professionals, and partners with IT to deliver data engineering, MLOps, and application development solutions.

Requirements

Working knowledge of modern cloud data platforms and multi-layer data warehouse architectures; Snowflake experience preferred.
Command of data science, statistical analytics with applied experience in regression and gradient-boosted methods, time-series forecasting, and optimization.
A strong foundation in Python, R, and SQL with practical analytics engineering knowledge, including dbt, advanced SQL, curated data models, metric and semantic layer definition, and automated data quality testing.
Deep experience with enterprise BI, including dataset design, performance tuning, governed self-service, and release management.
Familiarity with geospatial analytics, GPS and telematics data, and route / logistics optimization engines.
Practical understanding of generative AI in an enterprise setting. Informed point of view on agentic AI, including multi-step agent design with the judgment to know where these approaches outperform a conventional model and where they do not.
Understanding of MLOps practices, including model registry, feature store, retraining pipelines, and drift monitoring.
Strong business acumen and the credibility to work with field operations leaders and technical practitioners.
Demonstrated ownership of an analytics function serving a large operational or field-based business such as waste, logistics, transportation, distribution, utilities, field services, or manufacturing.
Track record of delivering machine learning into production and sustaining it, including monitoring, retraining, drift management, and handoff to engineering.
Experience running enterprise analytics as a product at scale.
Hands-on experience deploying generative AI or large language model solutions into enterprise production.
Experience leading through other managers within a large, matrixed organization with a history of partnering with Information Technology, Enterprise Data, and business stakeholders.
Experience building and championing a resourcing case covering headcount, contract labor, funding sources.
10+ years of progressive experience in data science, advanced analytics, or business intelligence.
5+ years of experience leading teams, including 2+ years leading through other managers.
Demonstrated experience delivering machine learning or advanced analytics solutions into production use.
Excellent cross-organizational communication skills.

Nice To Haves

Snowflake experience preferred.
Route-based or fleet operations experience preferred; geospatial analytics, telematics, and routing optimization experience.
Snowflake, dbt, and enterprise-scale Power BI experience.

Responsibilities

Lead the Operations Analytics organization, roadmap, and product portfolio; including, data science, BI and data analytics products such as volume forecasting and staffing, route standards, and coaching insights.
Serve as the analytics partner and primary point of contact to Operations leadership, communicating results and providing consultative, analytic based, thought leadership to senior management and executive leaders in both operational and financial terms.
Leverage advanced analytics to analytically derive and validate key operating metrics and data attributes such as weight, service time, and disposal turn time.
Establish best practices for the function, including ML Ops, analytic development lifecycle, metrics definitions and BI dashboard composition, form, and function.
Direct the predictive and prescriptive modeling and evaluate model performance to retain accuracy and performance standards for monitoring, drift detection, and retraining.
Extend the portfolio into generative and agentic AI to drive effective outcomes, including extraction of unstructured data, automated summarization of operational reporting, and AI-generated coaching insights.
Partner with Information Technology and Enterprise Data to architect and engineer data platforms that enable Data Science and Advanced Analytics. Own the semantic layer and metric definitions across models and BI products.
Run the Operations BI suite, balancing new development against defect resolution and platform events in partnership with IT.
Champion data science and AI adoption across the enterprise.
Perform other job-related duties as assigned or apparent.