Data Scientist

Description

Data Scientist

About Asplundh Tree Expert, LLC

Asplundh Tree Expert, LLC is a leading provider of vegetation management, utility infrastructure services, and related field operations across North America. Our work supports critical electrical and communications infrastructure by helping utilities and municipalities improve safety, reliability, and operational performance. Data and analytics play a growing role in how we plan work, manage costs, optimize resources, and deliver high-quality service at scale.

About the Role

We’re looking for a Data Scientist to join Asplundh’s Technology / Data & Analytics organization with a focus on finance and operational performance. In this role, you will transform complex operational, workforce, and financial data into actionable insights and predictive solutions that improve decision-making, field efficiency, cost control, and service outcomes.

You will work closely with leaders across Operations, Finance, Fleet/Assets, Safety, HR/Workforce Planning, and Technology to define analytical problems, build models, and communicate results that drive measurable impact.

What You’ll Do (Responsibilities)

• Collect, clean, and preprocess datasets from multiple sources, including operational systems, workforce data, financial platforms, asset/fleet tools, and enterprise data warehouses.
• Analyze financial and field operational trends to identify cost drivers, productivity gaps, performance risks, and optimization opportunities.
• Build, validate, and maintain models for:
• Financial forecasting (revenue, cost, budget variance, margin drivers)
• Operational forecasting (workload, demand, staffing, resource needs)
• Efficiency and cost optimization (crew scheduling, routing, overtime reduction, asset utilization)
• Develop reproducible analytics and pipelines using Python/R and SQL, and follow strong documentation standards.
• Design and monitor key performance indicators (KPIs) for field operations, finance, safety, and service delivery, including root cause analysis for variance.
• Create dashboards, scorecards, and reports for Finance and Operations stakeholders that clearly highlight trends, risks, and recommended actions.
• Partner with business leaders to translate goals (cost control, productivity, throughput, reliability, safety outcomes) into measurable analytic questions.
• Present results to technical and non-technical audiences, explaining assumptions, uncertainty, and expected impact.
• Support experimentation and continuous improvement efforts (e.g., pilot evaluations, operational process changes).
• Work with data engineering and IT to transition analyses/models into production or operational workflows where applicable.

What We’re Looking For (Qualifications)

Required

• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, Finance, or a related field (or equivalent practical experience).
• 5-7 years in a Data Science or related position.
• Strong foundation in statistics and probability (hypothesis testing, distributions, regression).
• Proficiency in Python or R for analysis and machine learning.
• Strong knowledge of SQL for querying large datasets.
• Working understanding of machine learning methods and evaluation metrics.
• Ability to communicate clearly with Finance and Operations teams.
• Strong problem-solving and project execution skills.

Preferred / Nice to Have

• Experience in finance analytics, budgeting, forecasting, or cost modeling.
• Familiarity with operational domains such as field operations, utilities, workforce planning, logistics, asset/fleet management, or safety analytics.
• Experience with time-series, optimization, or geospatial analysis.
• Experience with BI/visualization tools (Power BI, Tableau, Looker).
• Familiarity with cloud or big-data platforms (Azure/AWS/GCP, Databricks, Spark) is a plus.
• Comfort with Git/version control and production-minded analytics practices.

Key Skills & Competencies

• Financial and operational analytical thinking
• Statistical reasoning and model interpretation
• Data wrangling and feature engineering
• Forecasting and predictive modeling
• KPI design and performance monitoring
• Data visualization and storytelling
• Collaboration in cross-functional environments
• Attention to detail, accuracy, and data governance

Tools & Technologies You May Use

• Languages: Python, R, SQL
• Libraries: pandas, scikit-learn, numpy, scipy, statsmodels, PyTorch/TensorFlow (as needed)
• Visualization: Power BI, Looker, matplotlib, seaborn, ggplot2
• Data Platforms: Snowflake, Oracle, SQL Server (depending on stack)
• Workflow: Git,

Success Looks Like

Within the first 3–6 months, you will:

• Deliver analytics that improve forecast accuracy, cost visibility, crew productivity, or operational efficiency.
• Build or enhance KPI dashboards used by field and finance leaders.
• Contribute to at least one high-impact model or analytical tool adopted by stakeholders.

•