Staff Applied Scientist, Personalization

oneapp; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Stock options

Health benefits

401(k) match

Remote-friendly (US)

Job summary

oneapp is seeking a staff-level applied scientist to own personalization systems across our all-in-one financial platform. The role blends research‑grade modeling with production deployment to shape how millions of users interact with banking, credit, savings, and other financial services.

You will lead end‑to‑end AI initiatives, collaborating with product, engineering, and analytics to build scalable personalization, search, and retrieval systems that enhance discovery and user satisfaction

Qualifications

• Seven+ years of experience building and productionizing ML/AI models with measurable impact.
• Hands-on experience with LLM-powered apps, agentic systems, and classical ML (ranking, recommendations, personalization).
• Collaborates with product, engineering, and analytics to deploy AI solutions.

Responsibilities

• Design, build, and deploy ML/AI models and LLM systems for customer-facing experiences.
• Develop personalization and recommendation systems across product surfaces.
• Design and optimize search/retrieval to improve discoverability and relevance.
• Lead AI initiatives from ideation to deployment, sharing best practices across the team.
• Productionize LLM-powered applications alongside traditional ranking, recommendations, and personalization.

Skills

LLM‑powered applications

Agentic systems

Ranking & personalization

Cross‑functional collaboration

Education

Degree in CS/Data science/math or related

Job description

Role overview

A staff-level applied scientist role within a consumer fintech's AI and machine learning group, owning personalization systems that touch every surface of an all-in-one banking, credit, savings, lending, investing, and crypto product. The position blends research-grade modeling with production deployment, shaping how millions of users discover and interact with financial services. It is well suited to a builder who wants to ship LLM, deep learning, and classical ML systems end to end.

Responsibilities

• Design, build, and deploy machine learning, deep learning, and large language model systems that shape customer experience, drive growth, and improve operational efficiency, partnering closely with product, engineering, and analytics.
• Develop personalization and recommendation systems that deliver dynamic, user-centric experiences across multiple product surfaces.
• Design and optimize search and retrieval systems to improve discoverability, relevance, and user satisfaction.
• Lead AI initiatives from ideation through deployment, setting technical direction and sharing best practices across the applied science team.
• Explore and productionize LLM-powered applications and agentic systems alongside traditional ranking, recommendation, and personalization models.

Requirements

• Seven or more years of experience building and productionizing ML and AI models that deliver measurable business impact.
• Hands‑on experience with LLM-powered applications, agentic systems, and classical ML techniques including ranking, recommendations, and personalization.
• Strong technical background supported by a degree in computer science, data science, applied mathematics, or a related field.
• Proven ability to collaborate with product, engineering, and analytics teams to bring AI solutions from concept to deployment.
• A builder-and‑executor mindset with strong drive and proactivity, comfortable operating in a fast‑moving environment.

Nice to have

• Experience applying AI to financial services or other regulated consumer products.
• Background in large-scale recommendation, search, or retrieval systems.

Benefits and work setup

Compensation includes a competitive base salary, stock options, and health benefits starting day one, alongside a 401(k) plan with company match. The role is remote-friendly within the US, with flexible time off and growth opportunities in a high-growth, mission-driven culture. The standard interview process runs through an initial talent-partner conversation, a technical or hiring-manager interview, a team interview, an executive interview, and an offer.