Senior Data Scientist
Content + Source + Freshness • 14 Feb 2026 • 95% confidence
Offer value
The role provides an excellent opportunity for hands-on data science applications with significant market relevance and career growth potential.
- Strong relevance in data science and climate analytics
- Hands-on work with machine learning and predictive modeling
- Opportunity for significant impact in utility management
Pros
- High demand for data-driven insights in climate resilience
- Opportunity to work with advanced AI/ML technologies
- Collaborative environment with impactful real-world applications
Cons
- Requires a strong mathematical and analytical background
- High competition among candidates with similar qualifications
- Unclear salary and benefits information
Who it's for
Senior • Remote / In-office
Good fit
- Experienced data scientists
- Professionals proficient in Python and SQL
- Candidates passionate about sustainability and data analysis
Not recommended for
- Beginners in data science
- Individuals lacking programming skills
- Candidates uninterested in climate implications of data
Motivation fit
Key skills
About the job
About AiDASH
AiDASH is an enterprise AI company and the leading provider of vegetation risk intelligence for electric utilities. Powered by proprietary VegetationAI™ technology, AiDASH delivers a unified remote grid inspection and monitoring platform that uses a SatelliteFirst approach to identify and address vegetation and other threats to the grid. With a prevention-first strategy to mitigate wildfire risk and minimize storm impacts, AiDASH helps more than 140 utilities reduce costs, improve reliability, and lower liability across their networks. AiDASH exists to safeguard critical utility infrastructure and secure the future of humanAIty™. Learn more at www.aidash.com.
We are a Series C growth company backed by leading investors, including Shell Ventures, National Grid Partners, G2 Venture Partners, Duke Energy, Edison International, Lightrock, Marubeni, among others. We have been recognized by Forbes two years in a row as one of “America’s Best Startup Employers.” We are also proud to be one of the few software companies in Time Magazine’s “America’s Top GreenTech Companies 2024”. Deloitte Technology Fast 500™ recently ranked us at No. 12 among San Francisco Bay Area companies, and No. 59 overall in their selection of the top 500 for 2024.
Join us in Securing Tomorrow!
The Role
How you'll make an impact:
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Extract, clean, and analyze large datasets from multiple sources using SQL and Python/R.
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Build and optimize data scraping pipelines to process large-scale unstructured data.
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Perform statistical analysis and data mining to identify trends, patterns, and anomalies.
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Develop automated workflows for data preparation and transformation.
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Conduct data quality checks and implement validation procedures.
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Build and validate ML models (classification, regression, clustering) using TensorFlow, Keras, and Pandas.
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Apply feature engineering to enhance accuracy and interpretability.
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Execute experiments, apply cross-validation, and benchmark model performance.
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Collaborate on A/B testing frameworks to validate hypotheses.
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Work on predictive analytics for wildfire risk detection and storm damage assessment.
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Translate complex business requirements into data science solutions.
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Develop predictive models and analytical tools that directly support CRIS decision-making.
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Create interactive dashboards and automated reports for stakeholders.
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Document methodologies and maintain clean, production-ready code repositories.
What we're looking for:
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4–5 years of hands-on experience in data science, analytics, or quantitative research.
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Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or related field.
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Strong programming skills in Python for data analysis and machine learning.
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Proficiency in SQL and experience with relational databases.
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Experience with ML libraries (scikit-learn, pandas, NumPy, Keras, TensorFlow).
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Knowledge of statistical methods (clustering, regression, classification) and experimental design.
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Familiarity with data visualization tools (matplotlib, seaborn, ggplot2, Tableau, or similar).
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Exposure to cloud platforms (AWS, GCP, Azure) is a plus.
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Proven experience with end-to-end model development (from data exploration to deployment).
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Track record of delivering actionable insights that influenced business decisions.
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Strong analytical and problem-solving skills with attention to detail.
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