Data Scientist — Machine Learning Practitioner

About the role

The role involves building, validating, and improving machine learning and statistical models used in BlueConduit's infrastructure analytics products. It also includes helping design, build, and launch new model products and model classes, improving data science workflows, and communicating model uncertainty and tradeoffs clearly.

Responsibilities

• Build, validate, and improve machine learning and statistical models used in BlueConduit’s infrastructure analytics products
• Help design, build, and launch new model products and model classes that broaden the assets and risks BlueConduit can predict
• Improve data science workflows, model evaluation, reproducibility, and handoffs into software/product systems
• Work with heterogeneous municipal, infrastructure, geospatial, and field-observation datasets to generate actionable risk predictions
• Design validation approaches and communicate model uncertainty, limitations, and tradeoffs clearly to internal teams and customers
• Use modern AI coding tools such as Claude Code, Codex, or similar systems to accelerate development while applying strong independent programming judgment
• Use multiple AI agents to contribute to extremely robust workflows and code pipelines with built-in testing and reviews
• Support customer-facing analysis and present findings in ways that are clear, accurate, and useful for nontechnical decision-makers
• Contribute to R&D that scales the impact, reliability, and reach of BlueConduit’s predictive methods

Requirements

Strong Python-based data science experience, including pandas, NumPy, scikit-learn, and production-quality analysis workflows. An undergraduate degree in a quantitative field (e.g., CS, math, stats, physics). Experience building, validating, and improving machine learning or statistical models on messy real-world data. Experience building repeatable data science workflows in a product at a SaaS company or similarly operational environment. Ability to communicate modeling results, uncertainty, and tradeoffs clearly to technical and nontechnical stakeholders. Fluency using modern AI coding tools – including coordinating work of AI agents – to accelerate development, grounded in strong independent programming ability and judgment. Strong data visualization, verbal communication, and written communication skills. Comfort with Git-based development workflows. Attention to detail, curiosity, and commitment to building models that are understandable, usable, and trusted by the people making infrastructure decisions. Passion for socially impactful data science, environmental justice, and public-interest technology.

Qualifications

• A rigorous graduate degree in a quantitative field, or equivalent applied experience
• Experience modeling asset classes beyond BlueConduit’s current water distribution portfolio, such as fire risk, wastewater, hydraulic systems, climate risk, insurance risk, or other infrastructure domains
• Experience with geospatial data, GIS systems, GeoPandas, or spatial modeling
• Experience creating a new model product or extending an existing model product to a new domain or asset class
• Experience with both global/cross-location models and local/site-specific models
• Experience with methodologies beyond classical ML, such as neural networks, transformers, transfer learning, or other modern ML approaches
• Familiarity with infrastructure, water quality, government data, or regulated public-sector decision environments
• Experience working in Agile product development environments
• Aptitude and interest in building with rapid iteration cycles involving prototyping, receiving feedback, and rebuilding

Skills

• Python-based data science experience
• Experience with pandas, NumPy, scikit-learn
• Experience building and validating machine learning or statistical models on messy real-world data
• Experience building repeatable data science workflows in a product at a SaaS company or similarly operational environment
• Ability to communicate modeling results, uncertainty, and tradeoffs clearly to technical and nontechnical stakeholders
• Fluency using modern AI coding tools – including coordinating work of AI agents – to accelerate development, grounded in strong independent programming ability and judgment
• Strong data visualization, verbal communication, and written communication skills
• Comfort with Git-based development workflows
• Attention to detail, curiosity, and commitment to building models that are understandable, usable, and trusted by the people making infrastructure decisions
• Passion for socially impactful data science, environmental justice, and public-interest technology

Benefits

• Compensation: $140,000 – $150,000, commensurate with experience
• Equity options
• Health, vision and dental benefits
• Simple IRA benefit with company contribution matching