Senior Data Scientist

Description

Headquartered in Silicon Valley, California, Archer is a leader in the next-gen aerospace sector building an end-to-end advanced air mobility platform that delivers air taxis, unmanned aircraft systems (“UAS”), aviation-related physical artificial intelligence (“AI”) solutions, and other technologies to customers worldwide across the commercial aerospace and defense sectors.

Our sights are set high and our problems are hard, and we believe that diversity in the workplace is what makes us smarter, drives better insights, and will ultimately lift us all to success. We are dedicated to cultivating an equitable and inclusive environment that embraces our differences, and supports and celebrates all of our team members.

Archer is best known for Midnight, our electric aircraft. This role isn't on that program. The AI Products Org builds consumer software for the general aviation industry. There's a lot we're not ready to talk about publicly yet, so this posting stays vague on purpose. What we can say: the products are real, they ship, and people use them every day.

As a Senior Data Scientist here, you’ll be the bridge between raw, messy aviation data and the intelligence that powers our AI platform. You aren’t just building models; you’re taking high-frequency aircraft telemetry and noisy air traffic control audio and figuring out what's actually happening in the sky.

AI Products is a roughly 100-person software org inside a company of aerospace engineers, and we expect to grow substantially over the next year. You get the resources and momentum of a real org, but the products themselves are early. If you want to hear more about what we're building before you apply, you can't. Apply, and we'll tell you plenty.

Please note that this job description is intended to provide a general overview of the position and does not include an exhaustive list of responsibilities and qualifications

At Archer we aim to attract, retain, and motivate talent that possess the skills and leadership necessary to grow our business. We drive a pay-for-performance culture and reward performance that supports the Company’s business strategy. For this position we are targeting a base pay between $145,000.00 - $180,000.00. Actual compensation offered will be determined by factors such as job-related knowledge, skills, and experience.

Certain positions may be eligible for visa sponsorship.

Responsibilities

• Derive new signals from high-throughput, real-time data streams, inferring complex physical and operational states that are not directly observable in the raw telemetry.
• Build, evaluate, and iterate on models across diverse modalities, including dense time-series, spatial data, and highly noisy, unstructured sequential data.
• Design evaluation for problems with little or no labeled ground truth, using weak supervision, and programmatic labeling — and defend those definitions to engineers and domain experts.
• Partner with backend and platform engineers to take models from offline analysis into production streaming pipelines, and own their behavior once they are live.
• Work directly with aviation subject-matter experts to turn operational knowledge into features, labels, and evaluation criteria.
• What You Need:
• BS/MS/PhD degree in Computer Science, Statistics, Applied Mathematics, Operations Research, Aerospace Engineering, or a related quantitative field.
• 2+ years of applied data science or machine learning experience, including models that have run in production.
• Strong Python and strong SQL over large analytical datasets.
• Depth in time-series and sequence modeling.
• Experience working with sparse, incomplete, or unlabeled data — weak supervision, programmatic labeling, and building evaluation sets where no ground truth exists.
• Experience designing data validation and drift detection for continuously arriving data.
• Able to leverage AI assistants to increase velocity while fully understanding and taking absolute ownership of every model, feature, and analysis you ship.
• Strong communication skills and a collaborative mindset, including the ability to explain methodology and its limitations to a non-specialist audience.
• Bonus Qualifications:
• Prior experience or a deep interest in aerospace, aviation, or high-throughput tracking systems — including familiarity with flight telemetry, ATC phraseology, or airspace procedures.
• Familiarity with geographic/spatial data processing — projections, spatial indexing, and great-circle geometry.
• Awareness of distributed messaging and stream-processing systems (i.e. Apache Pulsar or Kafka, Apache Flink) and columnar or lakehouse analytical storage.