This opportunity entails being responsible for designing and implementing data science solutions that address business challenges. You will collaborate with stakeholders to define analytical goals, build predictive models, and deploy scalable solutions using modern data science tools and techniques. The role includes mentoring junior team members, optimizing data workflows, and integrating advanced technologies such as cloud computing, AI/ML, and real-time analytics into business processes. This role builds on foundational skills and introduces broader responsibilities in technical execution, problem-solving, and team collaboration.

What You'll Do:

• Analyzes large datasets to identify trends, patterns, and actionable insights.
• Builds and validates predictive models using statistical and machine learning techniques.
• Collaborates with stakeholders to define problem statements and data requirements.
• Develops and maintains data pipelines and model deployment workflows.
• Conducts A/B testing and evaluates model performance.
• Mentors junior team members and contributes to knowledge sharing.
• Presents findings to technical and non-technical audiences.
• Performs other duties as assigned.

What You'll Need:

• Bachelor’s degree in data science, statistics, computer science, computer engineering, or information systems and 2 years of relevant experience, or
• Master’s degree in data science, statistics, computer science, computer engineering, or information systems and 1 years of relevant experience, or
• In lieu of education, 6 years of relevant experience

What You'll Bring:

• Experience applying data science to infrastructure/transportation use casessuch asasset analytics, deterioration modeling, resilience, or risk assessment.
• Experience with statistical modeling, regression, and travelbehaviorand survey data.
• ProficiencywithGIS,Python, R, SQL,or ETL pipelinesand experience communicating results throughArcGIS,Power BI, Tableau, or similar tools.
• Demonstrated experience with machine learning algorithms and applied model development (e.g., k-NN, Naive Bayes, SVM, Decision Forests, etc.).
• Strong stakeholder communication skills and comfort translating analytical results into actionable guidance.

What We Prefer:

• Master’s degree in data science, statistics, computer science, computer engineering, or information systems

Additional Information

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