Staff Data Scientist, Personalization & Intelligence

Spring Health is a global mental health company on a mission to eliminate every barrier to mental health. We're building a world where getting support is simple, personal, and built around the person, so care can continue through every job, move, health plan, and life stage.

Our AI-native platform helps us deliver personalized support across self-guided tools, coaching, therapy, medication management, and specialty care. With outcomes independently validated by JAMA Network Open and the Validation Institute, Spring Health reaches more than 170 million people worldwide through leading employers, health plans, and partners.

As an AI-native company, we believe technology should expand the reach, quality, and humanity of care. Every Spring Health team member is expected to use AI tools thoughtfully, apply human judgment to AI outputs, and keep building AI fluency in ways that support their role and our mission.

Reporting to the Senior Director, Engineering in Customer Value, this Staff Data Scientist will architect the critical semantic and intelligence layers required to make our profile data consumable for AI-native development and downstream product features.

Please note that this is a hybrid role based in San Francisco, with an expectation to be in the office 2–3 days per week at our 44 Montgomery Street location. Candidates must be based in the San Francisco metro area or able to relocate independently within 90 days of their start date. Occasional travel will be required for team on-sites. 

• →Take ownership of a team’s semantic and intelligence architecture—providing system-wide design guidance for profile data (member, customer, provider) and ensuring robust data contracts across platform teams.
• →Use AI to explore and validate architectural patterns and decisions; design AI-augmented workflows (e.g., automated code generation, incident analysis) that multiply team output and enable engineers at all levels to work more effectively, specifically for personalized decisioning systems, agentic workflows, and real-time inference serving on AWS.
• →Establish guardrails for safe and responsible AI usage across your team, covering security, compliance, and output correctness, specifically for LLM-based systems, ensuring compliance and correctness.
• →Critically influence large, cross-team projects—ensuring execution without delay or compromise, and anticipating and removing barriers of all kinds.
• →Bring critical thinking and drive shared understanding in ambiguous situations; given a clear vision, create a strategy; given a clear strategy, define a sequence of objectives, specifically for the core intelligence and recommendation engine.
• →Develop strong working relationships cross-functionally and with business stakeholders; make sound tradeoffs between competing needs across short- and long-term horizons.
• →Empower data scientists and engineers to set ambitious goals and turn vision into reality; mentor and develop engineers across the organization, not just your immediate team, mentoring on DS/ML best practices.
• →Participate in an on-call rotation; push for better preparation and planning to reduce the frequency and impact of production incidents.

• Architectural Ownership – You are responsible for parts of our semantic layer and intelligence engine architecture. Your designs are exemplary—others reference them as the standard for engineering excellence across the org.
• AI-Augmented Engineering – You design workflows and tooling that make AI a force multiplier for your team, transitioning from rules-based heuristics to predictive ML and deep learning in the personalized decisioning layer and ensuring that the tools your team develops are usable in AI native flows. You establish guardrails that keep AI usage safe, compliant, and high-quality.
• Strategic Clarity – You turn vision into strategy and strategy into objectives. You bring structure and shared understanding to the most ambiguous problems on your team and across the org. You shape technical design and requirements, bringing together diverse stakeholder constraints.
• Cross-Org Influence – You drive best practices beyond your immediate team. Engineers and leaders across the organization seek your input on the hardest technical problems.
• People & Org Health – You empower others to do their best work—providing proper context, mentoring across levels, and fostering an inclusive, high-trust culture.
• Operational Reliability – You handle escalations reliably and lead high-stakes investigations with calm, clear communication. You drive systemic improvements to reduce future incidents.
• Engineering Excellence – You lead large-scale initiatives to reduce tech debt, improve architecture, and raise the quality bar, ensuring latency and scalability targets for production AI systems. You leave every system better than you found it.

• 8+ years of experience building production ML/AI systems, inference microservices, and real-time data pipelines
• Hands-on experience with Snowflake and OpenSearch for profile data/vector search
• Experience with LLM orchestration (LangGraph, LangSmith)
• Proven track record at the Staff level of operating effectively in highly ambiguous territories, investigating emerging technologies, and aligning diverse stakeholders around a clear technical strategy
• Demonstrated ability to mentor and develop engineers across the organization
• Adept at covering the breadth of technology while diving deep into architectural complexity and scalability for data flows, model pipelines and inference endpoints and management
• Track record of technical leadership: creating clarity from ambiguity, defining strategy from vision, and driving org-wide architectural improvements
• Advanced degree (MS/PhD) in Computer Science, Machine Learning, or related field

The target base salary range for this position is $239,000 - $270,000, and is part of a competitive total rewards package including equity and benefits. Individual pay may vary from the target range and is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. We review all employee pay and compensation programs annually using Radford Global Compensation Database at minimum to ensure competitive and fair pay.