Senior Data Scientist

Sundt Construction•Tempe, AZ

About The Position

As a 100% employee-owned contractor, when you work at Sundt, you’re not just hiring on at a company, you’re joining a culture. Because everyone at Sundt is part owner, you’ll join a team of people who are deeply invested in their work. From apprentices to managers, we’re passionate about the details and deliberate in everything we do. At Sundt we focus on building long-term prosperity for our clients, communities, and employee-owners. We offer competitive pay, industry-leading benefits including a 401k and employee stock ownership plan, incentive programs for craft and administrative employees as well as training that focuses on your personal and professional growth. We’re driven by skill, grit and purpose. Join us as we strive to be the most skilled builder in America. Job Summary As a Senior Data Scientist, you will serve as the technical lead for designing, building, and operationalizing advanced analytics and machine learning (ML) solutions that drive business value across the organization. As the first Data Scientist on the team, this role will establish foundational data science and ML capabilities, including setting up development environments, defining standards, and implementing best practices. In this role, you will partner closely with business and operational leaders to apply hypothesis-driven analysis, statistical modeling, and machine learning techniques to complex business problems, delivering actionable insights, analyses, and recommendations that directly support senior leadership decision-making. This role requires close collaboration with the Al team, Data & Analytics (D&A) organization, and business stakeholders to translate complex business problems into scalable, data-driven solutions. The ideal candidate combines strong statistical and machine learning expertise with practical engineering skills and the ability to communicate insights effectively to technical and non-technical audiences. Continuous learning, experimentation, and innovation are essential to advancing maturity in this area.

Requirements

3+ years of hands-on experience using Databricks or similar tooling for data science or machine learning workloads, including Spark-based data processing.
6-8+ years of experience in data science, advanced analytics, or applied machine learning roles.
Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field; or equivalent combination of education and experience.
Demonstrated ability to own solutions end-to-end, from problem definition through deployment and ongoing optimization.
Excellent verbal and written communication skills, with the ability to explain complex concepts to non-technical audiences.
Proven experience designing, deploying, and maintaining statistical and machine learning models in production environments.
Strong analytical, problem-solving, and critical-thinking skills with attention to detail.
Strong proficiency in Python and SOL for data analysis, modeling, and experimentation.
Strong understanding of a wide range of statistical and machine learning techniques, with the ability to select appropriate approaches based on business and technical constraints.

Nice To Haves

Azure-based data platforms
Mlflow
Git/GitHub
Azure DevOps
Cl/CD for ML (MLOps)

Responsibilities

Design, build, evaluate, and iterate on statistical, machine learning, and Al models using appropriate techniques and rigorous validation methods.
Design, develop, and deploy machine learning and advanced analytics solutions to solve complex business problems across the organization.
Develop and maintain documentation for models, assumptions, methodologies, experiments, and results to support transparency, reuse, and long-term sustainability.
Ensure data privacy, security, and compliance requirements are met when developing and deploying analytical solutions.
Establish, maintain, and own the data science and ML development environment, including tooling, libraries, workflows, and best practices within Databricks or other selected tooling.
Influence stakeholders by clearly communicating insights, model outcomes, risks, and recommendations through visualizations, presentations, and storytelling.
Lead and partner with the Al team and D&A organization to design end-to-end ML solutions, from data exploration and feature engineering to model training, deployment, and monitoring.
Partner closely and provide technical direction to Data Engineers and Data Architects to ensure data availability, quality, scalability, and alignment with enterprise data standards.
Perform exploratory data analysis, feature engineering, and large-scale experimentation to identify trends, patterns, and predictive signals within enterprise datasets.
Support the operationalization of models, including monitoring performance, managing model drift, and enabling retraining strategies.
Translate business questions and operational needs into analytical strategies, modeling approaches, and measurable outcomes that support decision-making.