What You'll Do
- Develop machine learning models that detect underperformance, faults, and anomalies in PV and storage system time-series data
- Build diagnostic logic that distinguishes root causes (soiling, shading, inverter clipping, string outages, communication gaps, sensor drift, degradation) of underperformance.
- Own the data science lifecycle from problem definition and model development through validation and monitoring, partnering with Engineering to deploy models into production.
- Help shape the technical direction for the detection and diagnostics roadmap and strengthen the team’s approach to model design, validation, and testing
- Partner with engineering to integrate your models into Resolv, turning model outputs into concrete, prioritized dispatch recommendations
- Close the loop with operations: track how your recommendations perform in the field — truck rolls avoided, issues resolved faster — and use that data to improve your models
- Explore and prototype new modeling approaches and validate their business impact before investing in production deployment
- Stay current with advances in ML and energy analytics, and share what you learn with the team
Lead Issue Detection & Diagnostic Models (60%)
Partner to Deploy High-Impact Models (20%)
Ideate and Explore New Models (20%)
Who You Are
- You own outcomes and measure success by the decisions your models improve
- You are rigorous with messy, real-world data. You dig into the quality, provenance, and business context of the data your models consume
- You take your models through the full lifecycle (from problem framing, prototyping, and validation to deployment and monitoring)
- You translate across domains. You work fluently with engineers, operations teams, and customers to scope what data science can (and can't) solve, and you can explain a diagnostic model to someone who has never heard of state-space methods.
Experience You’ll Need
- MS in a quantitative field or equivalent experience; 6+ years in applied data science
- Strong hands-on experience with time-series methods: anomaly detection, forecasting, seasonal decomposition, state-space models
- Production ML experience including model deployment, monitoring, and retraining pipelines
- Proficiency in Python (pandas, numpy, scikit-learn) and SQL; experience with at least one of PyTorch/TensorFlow
- Experience working with noisy, irregularly sampled, multi-sensor data at scale
- Track record of leading modeling projects end-to-end — translating ambiguous business problems into measurable model outputs and shipping them to production
- Proven written and verbal communication skills
- The ability to work with large cross-functional team.
- Experience mentoring data scientists or engineers and raising team standards
- Experience with architecture and tooling decisions for production ML systems on a major cloud platform (e.g., AWS, Azure, GC)
Experience That’s a Plus
- GenAI: Experience developing with Claude Code or putting LLMs into operational workflows
- Solar Modeling: Familiarity with PV performance modeling tools (e.g., pvlib, PVsyst) or irradiance and weather datasets. Hands-on work with inverter, SCADA, or other industrial IoT data streams
- Tooling: Experience with Databricks, MLflow, or similar ML platform and orchestration tooling
- Forecasting: Experience with probabilistic forecasting or failure prediction — e.g., forecasting energy production, predicting part failure, or estimating remaining life
- Optimization: Experience with ranking, prioritization, or decision-optimization problems such as alert triage or field-dispatch scheduling
Logistics
- We plan to have this role start in early Fall 2026
- We are unable to provide sponsorship for this role, now or in the future
- Most of our roles offer the opportunity to work remotely
- If you are in the Seattle area, we offer a vibrant office space in the historic and beautiful Smith Tower, in the heart of Pioneer Square
- We prioritize applicants near one of our employee clusters and offer one or more local gatherings per year
Work-Life & Culture
- We provide outstanding benefits including family medical, dental, vision, disability, 401(k) administration and $1k match per year and thoughtful paid time off
- We offer 12 weeks of paid parental leave to all FTE employees (birthing and non-birthing) after 1 year, and four-week paid sabbatical leave after four years
- We offer a competitive total compensation package that includes monthly health insurance premiums, bonuses and long-term stock options for every employee
- We love to lift each other up through company-wide slack channels such as #puppiesandpets, #omnidian-wellness, #praiseandbooms and #sustainablefuture
- We have affinity groups to help employees feel seen and supported, such as Rainbow Array, Black Lights Matter, Neurospicy R Us, Puente and more.
- We are a passionate, mission driven team that believes in collaboration, mutual respect and trust. For examples, come Discover our Story!
Grow with Us
- We mentor and invest in our employees and prioritize them for future opportunities. Check out our Instagram reels to see a few career journey examples, or this Omnidian Career Experience overview on YouTube.
- Internal candidates: Check out our advice on Internal Transfer: Job Application Process
- Here are the roles in this career track:
- Senior Data Scientist
- Staff Data Scientist
- Principal Data Scientist
- We’re a fast-growing growth company, which means we’re constantly reinventing processes, adding new products, and asking people to use all of their skills and talents. That means there’s going to be a lot of opportunities for you to grow, which also means you will likely be stretched in ways you’ve never experienced in a job before. If you are resilient, determined, and not afraid of a big challenge, come apply.
Skills Required
- MS in a quantitative field or equivalent experience
- 6+ years in applied data science
- Hands-on experience with time-series methods (anomaly detection, forecasting, seasonal decomposition, state-space models)
- Production ML experience including model deployment, monitoring, and retraining pipelines
- Proficiency in Python (pandas, numpy, scikit-learn) and SQL
- Experience with at least one of PyTorch or TensorFlow
- Experience working with noisy, irregularly sampled, multi-sensor data at scale
- Track record of leading modeling projects end-to-end and shipping to production
- Proven written and verbal communication skills
- Ability to work with large cross-functional teams
- Experience mentoring data scientists or engineers and raising team standards
- Experience with architecture and tooling decisions for production ML systems on a major cloud platform (e.g., AWS, Azure, GCP)
- Experience developing with Claude Code or putting LLMs into operational workflows (GenAI)
- Familiarity with PV performance modeling tools (pvlib, PVsyst) or irradiance/weather datasets; hands-on with inverter, SCADA, or other industrial IoT streams
- Experience with Databricks, MLflow, or similar ML platform and orchestration tooling
- Experience with probabilistic forecasting, failure prediction, or remaining-life estimation
- Experience with ranking, prioritization, or decision-optimization (alert triage, field-dispatch scheduling)
What We Do
Omnidian’s mission is to protect and accelerate capital invested in residential and commercial solar by homeowners and businesses across the nation. Our proprietary technology monitors your system 24/7, covers maintenance for all hardware and software components, and includes the industry’s leading Cash-Back Energy Guarantee. If your system underperforms, we’ll make it right, and compensate you for energy lost. The award-winning Solar Experts in our Seattle Operations Center are available toll-free and have live, real-time access to your solar performance data. Today, we are responsible for over 200,000 solar sites nationwide including the large-scale portfolios of our Fortune 1000 clients. And, we’ve been named one of the Top 100 Companies To Work For by Seattle Business Magazine for two years running.


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