Senior Data Scientist (Full lifecycle delivery)

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Senior Data Scientist (Full lifecycle delivery)

Location: Remote, PST candidates only

6-12 months

Target rate is $75/hr., all-inclusive

Power utilities industry

Initial Scope of Work

Department Overview

This team develops machine learning solutions that convert aerial and inspection imagery into actionable insights. The team works cross-functionally across product, inspection, data science, machine learning engineering, and business stakeholders to deliver scalable analytics capabilities that improve safety, asset visibility, and operational decision-making. The group emphasizes rigorous model development, structured evaluation, and disciplined production practices to move solutions from concept through operational use.

Position Summary

We are seeking a senior-level data scientist with strong computer vision expertise to deliver models across the full development lifecycle, from business scoping and labeling strategy through training, validation, deployment, production inference, user-facing reporting, and release. In this role, you will translate high-priority inspection and asset intelligence needs into production-ready model capabilities using a standardized, stage-gated operating model focused on quality, documentation, and stakeholder alignment.

What You Will Do

• Develop, validate, and operationalize scalable computer vision models that support inspection and asset intelligence use cases such as infrastructure condition assessment and anomaly detection
• Translate prioritized business needs into clear problem statements, class definitions, image-domain boundaries, and structured model development plans prior to labeling and training
• Design image sampling and labeling strategies, create annotation guidance, run pilot workflows, and establish quality thresholds that enable production-ready datasets
• Manage production labeling workflows including job planning, quality control checkpoints, relabeling processes, and dataset performance monitoring
• Build and iterate model training pipelines from initial baselines through release candidates, using structured experimentation, validation metrics, and systematic error analysis
• Evaluate models using held-out datasets and business-aligned aggregation logic to ensure real-world performance aligns with production expectations
• Provide technical recommendations on model architecture, training strategies, deployment readiness, and downstream logic based on evidence and business needs
• Integrate approved models into scalable inference pipelines across development and production environments, including deployment validation and monitoring
• Build and validate user-facing reporting logic so stakeholders can confidently consume model outputs
• Partner closely with product managers, domain experts, machine learning engineers, labeling teams, and stakeholders to align on scope, performance thresholds, timelines, and release readiness
• Maintain disciplined documentation across model artifacts, experiments, quality summaries, deployment notes, and release materials

What You Bring

• Master’s degree or PhD in data science, machine learning, computer science, engineering, mathematics, statistics, applied sciences, or a related quantitative field, or equivalent experience
• Three or more years of experience in computer vision, machine learning, image processing, or related analytical product development
• Strong experience developing and evaluating deep learning models, including object detection, classification, and related computer vision techniques
• Advanced programming skills in Python, with experience working in version-controlled development environments and model pipelines
• Experience translating business requirements into technical specifications, data strategies, validation plans, and measurable success criteria
• Experience designing testing methodologies, performance evaluation approaches, and error analysis frameworks for production-oriented machine learning systems
• Ability to work effectively across cross-functional teams and communicate clearly with technical and non-technical stakeholders
• Strong analytical, problem-solving, presentation, and documentation skills
• Proficiency with standard business tools to prepare reports, analyses, and stakeholder-ready materials

Desired Qualifications

• Experience working with large-scale image datasets and cloud-based machine learning platforms such as AWS, Azure, Google Cloud Platform, or similar environments
• Experience supporting models across the full lifecycle including labeling strategy, pilot validation, production deployment, inference operations, and reporting integration
• Familiarity with stage-gated model development, MLOps practices, and structured release processes for analytics products
• Background in infrastructure, industrial inspection, manufacturing quality, or other environments where image-based analytics support operational decisions