Junior Data Scientist

Who We Are

Arcadia is the AI-powered energy intelligence platform for businesses. We replace fragmented tools and manual workflows with one platform to pay utility bills, buy energy, and advance sustainability — across every location, at enterprise scale.

Trusted by Fortune 2000 companies, Arcadia combines unified data, AI-powered analytics, and expert advisory to help enterprise teams save money, mitigate risk, and cut carbon.

We Deliver This Through Three Comprehensive Solutions

• Utility Bill Management: Automating the entire utility bill lifecycle — from data capture and validation to payment processing and auditing.
• Energy Procurement Advisory: Bringing together comprehensive data, AI-powered analytics, market expertise, and a strong partner network to make sophisticated procurement options accessible to all. .
• Sustainability Reporting — Verified emissions data with seamless integration into leading sustainability platforms.
  
  

Tackling the world's most complex energy challenges requires diverse thinking. We're building teams of people from different backgrounds, industries, and disciplines — united by a belief that energy management should be simple, intelligent, and a genuine driver of business value.

The Role

We are looking for a Data Scientist to join our Applied AI team. This is a hands-on, generalist role working across Arcadia's core ML and AI workstreams: utility data extraction, consumption forecasting, and agent-powered production workflows. You will work directly with senior data scientists and engineering to build, evaluate, and ship models that drive meaningful impact on central business operations.

This role is a strong fit for someone early in their career who wants broad exposure to applied ML—from building classifiers and forecasting models to tuning prompts and supporting evaluation frameworks.

What You'll Do

• Develop and extend production ML models for utility bill data extraction, including classifier and routing logic, LLM extraction prompts, and template confidence and accuracy analysis
• Build and iterate on forecasting models for operational use cases: predicting bill availability, estimating utility spend, and surfacing prioritization signals
• Contribute to audit and anomaly detection work—analyzing flagged bills, quantifying error rates, and helping tune tolerance thresholds
• Participate in offline experimentation and champion/challenger evaluations; document results clearly and compellingly to guide business decisions
• Work with data teams and subject matter experts to source, understand, and validate data; surface data quality issues proactively
• Write clean, well-documented Python code; contribute to shared notebooks and model development pipelines
• Collaborate with engineering to support model integration and monitoring; contribute to design docs and relevant code reviews
  
  

What We're Not Looking For

This is not a research role, a pure NLP or LLM engineering role, or a data analytics/BI role. Candidates whose experience is primarily in dashboarding, reporting, or model-free data work are not a fit. Candidates who have only worked in academic or research settings without production deployment experience should be screened carefully.

What Will Help You Succeed

• 1–3 years of experience building and evaluating ML models in a professional or research setting
• Solid foundation in statistics, supervised learning, classification, regression, and evaluation methodology (precision/recall, cross-validation, holdout testing)
• Proficiency in Python (pandas, scikit-learn, numpy) and SQL
• Experience with time series data or forecasting is a plus
• Exposure to generative AI tools and LLM-based workflows
• Ability to work iteratively and independently—formulate assumptions, document them, and advance projects without constant direction
• Clear communicator: able to explain what a model does and what its results mean to both technical and non-technical stakeholders
• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field—or equivalent practical experience