Data Scientist

• Frame ambiguous revenue-cycle problems as tractable modeling problems and own them end to end such as claim and invoice viability scoring, denial and underpayment prediction, revenue/ARR prediction, and work prioritization from hypothesis to a validated, monitored result in production
• Build and validate models with statistical rigor: thoughtful feature design, sound handling of missingness and class imbalance, calibration, honest evaluation against simple baselines, and a clear treatment of uncertainty
• Design, build, and maintain the data pipelines feeding these models integrating with source collections, billing, and EHR systems and internal data stores, and handling data collection, cleansing, field mapping, and payer/plan normalization with a bias toward reliability and data quality
• Establish and own MLOps practice: model monitoring and drift detection (e.g., population stability), calibration and threshold selection, benchmarking against simple baselines, explainability (e.g., SHAP and interpretable coefficients), experiment and version tracking, and model documentation such as model cards
• Partner with subject-matter experts to encode business-rule and heuristic logic alongside statistical models where that produces a more accurate or more defensible result
• Gather and document business requirements for revenue-cycle initiatives, including workflows, decision points, stakeholder objectives, operational constraints, success criteria, data availability, and underlying business assumptions, and maintain traceability as those requirements evolve.

Senior AI Data Scientist

• Provide leadership to managers and supervisors for all daily operations of AVANGRID Networks Energy Control Centers.
• Responsible for maintaining the safety, reliability, and security of the Company's electric and gas transmission and distribution systems.
• Oversee the use of the Energy Control System (ECS) and Gas SCADA System.
• Develops departmental goals related to corporate goals and objectives.
• Lead the design and development of advanced ML/AI solutions (GenAI, LLMs, NLP, classical ML, causal inference) that directly support Avangrid Networks’ customer experience priorities: CSAT improvement, payment and collections, and contact deflection.
• Architect end-to-end ML systems on modern cloud platforms (Azure, Databricks), including feature stores, model registries, deployment pipelines, monitoring, and retraining workflows - establishing reference patterns for the broader Data Science & Analytics organization.
• Lead GenAI and agentic AI initiatives for both customer-facing (self-service) and agent-assist applications, including RAG architectures, retrieval pipelines, evaluation frameworks, and responsible AI guardrails for the flagship Iberdrola initiatives.
• Partner closely with Data Engineering to define data product requirements, drive customer-oriented data infrastructure build-out, and ensure analytics-ready datasets across SAP, CC&B, AMI, contact center, and digital platforms.
• Collaborate closely with technical leads in Customer Service, Elec/Gas Operations, Digital Platforms, Regulatory, and IT to translate business needs into AI/ML solutions and embed them into production workflows.
• Serve as the data domain expert (“Lead SME”) for customer, billing, contact, and consumption datasets—including data lineage, quality, and appropriate analytical use.
• Serve as technical lead and mentor for junior data scientists and analysts; review modeling approaches, code, and methodology; raise the technical bar across the team.
• Support regulatory filings, audits, rate cases, and PSC/DPU inquiries by delivering accurate, defensible, data-driven analysis and written narratives.
• Champion responsible AI practices, including model documentation, bias assessment, privacy compliance, and alignment with Avangrid’s AI governance and Global AI Inventory standards.

Principal, Data Science – Architecture

• Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements.
• Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs.
• Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services.
• Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments.
• Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals.
• Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost.
• Run A/B or uplift experiments to quantify impact and guide iteration.
• Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.

Ads AI Analytics Lead

• Provide advanced analytics expertise within the Business Analytics function
• Independently apply data science techniques to solve business problems
• Develop, validate, and maintain predictive models and AI solutions
• Utilize multiple sources of data and various machine learning techniques
• Write clean, modular, and well-documented code for production
• Interpret data and model outputs to generate actionable insights

Senior Data Scientist – Level I

• Lead the design, development, and deployment of advanced statistical, machine learning, and AI solutions—including LLM-powered applications—to solve complex business, workforce, and organizational challenges.
• Translate ambiguous business and HR questions into well-defined analytical approaches, scalable data products, and decision-support tools.
• Design and oversee end-to-end data science workflows, including data extraction (e.g., enterprise data warehouses), validation, modeling, deployment, and performance monitoring.
• Integrate data from multiple internal and external sources to create modeling-ready datasets, reusable data assets, semantic layers, and metadata frameworks that enable scalable and self-service analytics.
• Develop and productionize predictive and prescriptive models to explain outcomes, forecast behavior, and identify risks and opportunities.
• Build and deploy advanced AI solutions using modern frameworks (e.g., LLMs, embeddings, RAG architectures), and lead experimentation and rapid prototyping to evaluate emerging capabilities.
• Embed analytics and AI solutions into business processes through automation, system integration, and near real-time data capabilities.
• Partner with HR Business Partners, talent leaders, executives, data engineering, and IT teams to deliver actionable insights and ensure alignment with architectural, security, and data quality standards.
• Provide technical leadership across data science initiatives, ensuring consistency with best practices, methodologies, and quality standards.
• Communicate complex analytical insights and AI concepts clearly to non-technical stakeholders, influencing strategic and operational decision-making.
• Mentor and coach junior team members, elevating team capabilities in AI, machine learning, and analytics best practices.
• Ensure adherence to responsible AI principles, including data privacy, bias mitigation, security, and ethical use of employee data.
• Stay current on emerging AI and analytics trends, proactively identifying opportunities to incorporate new technologies into enterprise use cases.

Data Scientist jobs in Maine

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Senior AI Data Scientist

Principal, Data Science – Architecture

Ads AI Analytics Lead

Senior Data Scientist – Level I

Senior Data Scientist – AI, People Analytics