Job description

Location: San Francisco | In-Person 3–4 Days/Week 💡 ABOUT ARCH Arch powers frontline industries with cutting-edge AI. The US $800 billion frontline-services sector underpins the nation, yet many of these critical businesses still rely on spreadsheets, outdated software, and paper forms. We’re driving energy resilience, economic strength, and long-term prosperity by bringing AI innovation to the services that need it most—starting with home energy. We’re backed by Coatue, Floodgate, Gigascale, ReGen, MCJ Collective, and the founders of Aurora Solar. Compensation: Competitive salary + significant equity package based on experience. 🎯 ROLE OVERVIEW We’re hiring a Data Scientist to help architect the intelligence layer of Arch. You’ll work directly with our CTO and founding team to model customer behavior, optimize campaign performance, and build scalable, production-ready machine learning systems. You’ll have ownership from data collection to deployment, working in a highly iterative environment with direct feedback from real-world users. Your work will power high-stakes decisions that directly impact customer outcomes, product development, and revenue. This is a high-ownership, high-ambiguity role for someone excited to ship fast, learn from deployment, and move the needle in a mission-critical industry. 🧠 WHAT YOU’LL DO - Develop and deploy predictive models to identify high-conversion leads and drive campaign targeting - Own the full ML lifecycle: from data acquisition, cleaning, and labeling to modeling, validation, and deployment - Analyze product performance and customer usage to inform roadmap and product design - Collaborate with the engineering team to productionize ML pipelines and integrate them into our backend systems - Work closely with leadership to build internal analytics, dashboards, and investor-facing insights - Prototype, experiment, and iterate quickly—balancing rigor with speed 📚 QUALIFICATIONS Must-haves: - 3–6 years experience in data science, ML engineering, or analytics roles in fast-paced environments - Proficiency in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, or PyTorch) - Familiarity with data pipelines, model evaluation, and deployment workflows - Proven ability to work independently in ambiguous contexts with high judgment and ownership - Experience working with business stakeholders to translate messy data into actionable insights Strong bonuses: - Experience with large-scale structured data (e.g., property, utility, or energy-related datasets) - Knowledge of marketing analytics, targeting models, or customer segmentation - Familiarity with modern data tooling (e.g., dbt, Airflow, Dagster, DuckDB) - Comfort with basic full-stack workflows or collaborating in a product engineering environment 🌱 WHAT WE VALUE - First-principles thinking - Extreme ownership - Speed and bias for action - Low ego, high standards - Deep care for users 🧑‍🤝‍🧑 WHO YOU’LL WORK WITH Philipp (CEO) – Grew up on a Bavarian strawberry farm, built some of the world’s largest solar plants, crafted growth strategies for F500 software companies at McKinsey, and studied at Stanford. Known for breaking through walls and never taking no for an answer. Ask him about his favorite SF cafés. Sacha (CTO) – Product-minded engineer from Belgium and Australia with deep AI expertise and YC startup experience. Previously built wildfire response systems that save lives. Obsessed with world-class UX, lightning-fast execution, and excellence at the intersection of design and engineering. Ready to build the intelligence layer for a new class of frontline systems? Apply below and help us power the future of energy.

Requirements

Must-haves

Location: San Francisco | In-Person 3–4 Days/Week
This is a high-ownership, high-ambiguity role for someone excited to ship fast, learn from deployment, and move the needle in a mission-critical industry.
Develop and deploy predictive models to identify high-conversion leads and drive campaign targeting
Own the full ML lifecycle: from data acquisition, cleaning, and labeling to modeling, validation, and deployment
Analyze product performance and customer usage to inform roadmap and product design
Collaborate with the engineering team to productionize ML pipelines and integrate them into our backend systems
Work closely with leadership to build internal analytics, dashboards, and investor-facing insights
Prototype, experiment, and iterate quickly—balancing rigor with speed
3–6 years experience in data science, ML engineering, or analytics roles in fast-paced environments
Proficiency in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, or PyTorch)
Familiarity with data pipelines, model evaluation, and deployment workflows
Proven ability to work independently in ambiguous contexts with high judgment and ownership
Experience working with business stakeholders to translate messy data into actionable insights
Experience with large-scale structured data (e.g., property, utility, or energy-related datasets)
Knowledge of marketing analytics, targeting models, or customer segmentation
Familiarity with modern data tooling (e.g., dbt, Airflow, Dagster, DuckDB)
Comfort with basic full-stack workflows or collaborating in a product engineering environment
First-principles thinking
Extreme ownership
Speed and bias for action
Low ego, high standards
Deep care for users
Ready to build the intelligence layer for a new class of frontline systems?
Apply below and help us power the future of energy.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpythonsql

Platform: ashby
Effort: quick