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About This Role
About Omnidian
Omnidian, Inc. is a fast\-growing Series C tech\-enabled service company revolutionizing performance assurance for the distributed solar and energy storage industries. Omnidian is building a more sustainable future for the planet through our passionate teams, our innovative technology, and by creating an amazing customer experience. *We are a certified B Corp, headquartered in Seattle, WA* The Job
We are seeking aSenior Data Scientist to design, build, and scale advanced detection and diagnostic models for solar and storage assets. In direct support of Omnidian’s mission, your work will enable faster detection, fewer truck rolls, and better customer experience. As a key member of the data science team, you will report to the Director of Data Science and partner closely across product management, engineering, and operations to take models from idea to production in Omnidian’s Resolv platform.
### What You'll Do
At Omnidian we believe in trust and autonomy. How you create an impact is ultimately up to you. Here is an outline of some of the things you’ll be doing:
Lead Issue Detection \& Diagnostic Models (60%)
- 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 to Deploy High\-Impact Models (20%)
- 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
Ideate and Explore New Models (20%)
- 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
### 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.
$116,800 \- $146,000 a yearOur Philosophy on Pay: The range listed above reflects the starting base salary for most new hires in this role. However, your financial growth doesn't stop at the offer letter. We invest deeply in our people and reward results; annual merit increases can bring your total compensation above this initial range. Our goal is for you to continue growing within the pay band as you sharpen your skills and drive meaningful outcomes.
- Committed to Parity: We eliminate the "negotiation tax" by placing candidates within the band based on verified professional experience and skill set, not bartering ability, and by giving our best offer up front. This is a core part of our mission to ensure gender pay equity and fairness across the board.
- Comprehensive Health Coverage: Your well\-being is our priority. We cover 100% of health insurance monthly premiums for employees and 50% for your dependents.
Performance Bonus: Exceptional work deserves exceptional rewards. You’ll be eligible for bonuses that reflect your contributions to our collective success.
- Equity Stake: We offer stock options so you can share in the long\-term value you help create as we shape the future together.
- Continuous Growth: We are a learning organization. To support your evolution, we provide up to $500 in annual learning reimbursement for courses, certifications, or conferences.
\#LI\-REMOTE Privacy
*California\-based candidates: To understand more about the data we collect and process as part of your application, please view our California Job Candidate Privacy Policy.* *https://www.omnidian.com/privacy\-policy\-ca\-candidates/*
Diversity and Inclusion
*We strongly believe that diversity of experience, perspectives, and background will lead to a better environment for our employees and a better product for our customers. We are committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment. We value diversity and inclusion and are committed to ensuring our hiring and retention practices, as well as our office culture, reflects this value.*
*We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.*
*Omnidian is an equal opportunity employer. We are committed to diversity in the workplace. We make employment decisions on the basis of merit and business need. We hire without consideration to age, ancestry, citizenship, disability, gender expression, gender identity, marital status, national origin, political activity or affiliation, race, religion, sexual orientation, veteran status, or any other basis protected by law.*
We invite you to be part of our mission to create a workplace that is inclusive and welcoming to all.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $116K-$146K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Omnidian, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($131K) sits 32% below the category median. Disclosed range: $116K to $146K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Omnidian AI Hiring
Omnidian has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $146K - $146K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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