Assistant Engineer and Data Scientist — Intelligent Water System

Hazen and Sawyer is seeking an entry-level engineer for our Cincinnati/Columbus, Ohio office(s). As part of our design and process teams, you will work on water and wastewater projects, including drinking water/wastewater/reuse treatment facility upgrades, conveyance systems, wellfields, stormwater management, water resources, and asset management.

Why Hazen and Sawyer:

• Founded in 1951 by the son of Allen Hazen (developer of the Hazen-Williams equation), we are an employee-owned company with a singular focus on "all things water."
• Our work includes planning, design, and oversight of construction of environmental infrastructure – for water, wastewater, and stormwater management.
• We foster a work environment low on bureaucracy and high in creativity.
• We recruit talented professionals, provide them with challenging, interesting, and creative assignments, and furnish them with the tools they need to succeed.
• We currently have over 2,000 employees in 70+ offices throughout the United States, Latin America, Canada, and the UK.
• A flexible hybrid work schedule supports today's busy professionals.

Key Responsibilities:

• Develop innovative digital solutions for water systems focused on near-real-time sensor data curation, multimodal sensor fusion, digital twins, predictive control, and predictive maintenance of water related infrastructure
• Contribute to hydrologic, hydraulic, and hydrodynamic model development and calibration
• Develop automated quality assurance and control workflows for data ingestion, model validation, and calibration
• Assist in preparing engineering deliverables, presentations, and modeling and design reports · Design and implement closed-loop feedback and control logic bridging field instrumentation, edge computing devices, and SCADA/PLC systems
• Communicate complex AI/ML and control system approaches clearly to internal staff and external clients with varied technical backgrounds
• Keep up with the latest developments in artificial intelligence and machine learning models

Required Qualifications:

• Bachelor’s degree in environmental, civil, chemical, or mechanical engineering
• Strong grounding in fluid mechanics; exposure to computational fluid dynamics (CFD) — through coursework, research, or personal projects — is highly valued
• Proficiency in Python, including data processing and machine learning libraries (Pandas, NumPy, SciPy, scikit-learn); experience with C or C++ is a plus
• Strong communication skills, self-motivation, organization, and attention to detail
• Familiarity with programmable logic controllers (PLCs) and SCADA systems — from coursework, internship, or professional experience — including an understanding of how field devices communicate with control systems
• Hands-on experience with embedded systems or microcontrollers (Arduino, Raspberry Pi, or similar), including sensor interfacing, signal acquisition, and real-time processing
• Must be proficient in Microsoft Office Suite
• 0-2 years of relevant experience

Preferred Qualifications:

• Experience with SCADA systems and PLC logic and automation development
• Familiarity with industrial communication protocols (Modbus, DNP3, OPC-UA)
• Conducts self-quality reviews before submitting work for additional QA/QC
• Experience deploying code or models to cloud platforms (Azure, AWS, GCP) or containerized environments

What We Offer:

• Comprehensive health benefits (medical, dental, vision, and prescription plans)
• Pre-tax flexible spending plans for medical, dependent care, and transportation
• Short and long-term disability, and employer paid life insurance
• Paid holidays, floating holidays, and paid time off (PTO)
• Employer-contributed 401(k) plan and additional financial planning support
• Professional growth opportunities, including tuition reimbursement, in-house training, and incentives for professional registration and professional organization memberships
• Starting pay range for this position depends on skills, experience, education and geographical location