Data Scientist
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Full-time
Legal agreement: Employment
Compensation
USD144k - 168k/year
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2 months ago
Responsibilities
Data ScientistRemote, Anywhere in the USAbout AnswersNowAt AnswersNow, we are trailblazing the future of autism therapy, making it more immediate, accessible, and effective for families everywhere. Our innovative virtual ABA therapy platform is thoughtfully designed by clinicians to recreate the focused, supportive environment of in-person therapy, complete with distraction-free features and interactive activities that enhance engagement and progress.Our team operates fully remote—meaning you’ll have the flexibility to work from the comfort of home. If you're ready to make a meaningful impact and join a team that's reshaping autism therapy, we’d love to hear from you!Why this role matters:As our Data Scientist, you will optimize the smart systems that pull real-time clinical context and turn it into safe, accurate, and highly relevant recommendations. By bridging the gap between cutting-edge AI capabilities and deep clinical expertise, you ensure our models are deeply rooted in real-world care and held to the highest quality standards. Your work ensures that our digital systems are a reliable, trusted partner for our clinical teams, allowing us to safely scale our platform and deliver life-changing autism therapy to families nationwide.Job DetailsW2 EmployeeFull-Time100% RemoteJob Requirements4+ years of experience in applied data science, ML engineering, or AI engineering in a production environmentDeep understanding of RAG architectures: retrieval systems, embedding models, vector databases (Pinecone, Weaviate, pgvector, or similar), chunking strategies, and context assemblyExperience designing and running evaluation frameworks for AI systems — you've thought hard about how to measure quality in domains where ground truth is ambiguousStrong Python skills; experience with LLM orchestration frameworks (LangChain, LlamaIndex, or similar)Clinical NLP experience or healthcare AI background is strongly preferred — you understand why clinical data is different from general text and what that means for AI system designYou think like an engineer and a scientist: you build systems that can be measured, iterated on, and trusted — not black boxesStrong written communication: you can explain RAG pipeline design to a clinician and explain clinical requirements to an engineerGenuine interest in the clinical domain — you want to understand Applied Behavior Analysis well enough to build AI that actually helps BCBAs do their jobsNice to have:Experience with Amazon Bedrock, SageMaker, or AWS AI/ML servicesFamiliarity with HIPAA-compliant data handling for AI training and inference pipelinesBackground in clinical NLP, behavioral health informatics, or ABA/autism researchExperience with fine-tuning or RLHF — even if this role doesn't require it, understanding the tradeoffs informs better RAG designExposure to LLM-as-judge evaluation patterns or multi-model evaluation pipelinesWhat You’ll DoRAG Pipeline Design & OptimizationArchitect and continuously improve the RAG pipeline that retrieves client-specific clinical context — session notes, treatment plan goals, historical performance data — and injects it into inference-time promptsDesign the retrieval layer: chunking strategies, embedding models, vector store configuration, and retrieval ranking — optimizing for clinical relevance, not just semantic similarityBuild a context assembly system that selects and structures the most relevant clinical information for each model invocation, given token constraints and clinical priorityEvaluate retrieval quality rigorously: build test sets, measure recall and precision, and iterate on the pipeline based on where retrieval failsEvaluation Framework DesignDesign evaluation frameworks that assess AI recommendation quality beyond standard NLP metrics — working with clinical stakeholders to define what 'good' means for each use caseBuild automated evaluation pipelines that can test AI outputs at scale: LLM-as-judge evaluators, human review workflows, and clinical validity checksMaintain evaluation datasets that reflect the real distribution of clinical scenarios the model encounters in productionReport evaluation results in terms that clinical and product stakeholders can understand and act onModel Gap Analysis & MitigationSystematically identify where foundation model capabilities fall short for AnswersNow's care model: what clinical reasoning the model gets wrong, what it hallucinates, what it doesn't know how to handleFor each identified gap, recommend and implement the appropriate mitigation — improved retrieval, prompt engineering, output validation, or escalation to human reviewStay current on foundation model capabilities and evaluate new models against our clinical requirements as they emergeMaintain a gap log and roadmap that gives product and clinical leadership visibility into current AI limitations and the plan to address themProduction Monitoring & QualityMonitor production AI outputs for quality, drift, and failure modes using the evaluation infrastructure you've builtDefine alerting thresholds and escalation paths for when AI quality falls below acceptable clinical standardsPartner with the engineering team on observability — ensuring AI outputs are logged, traceable, and auditableConduct root-cause analysis when AI quality issues are reported and drive systematic fixesClinical & Cross-Functional PartnershipWork closely with clinical leadership and BCBAs to understand the care model deeply enough to design AI systems that support it accuratelyTranslate clinical domain knowledge into technical requirements: what context does the model need, what outputs are clinically acceptable, where does the model need to defer to the clinicianPartner with the BI Engineer and data team on the data infrastructure that feeds the AI pipeline — session data, outcomes data, treatment plan contentCommunicate AI system behavior clearly to non-technical stakeholders: what the system does, what it doesn't do, and where human judgment remains essentialWhat we Offer$144,000- $168,000 annual salaryFully remote – work from anywhere in the U.S.Flexible hours with an async-friendly team cultureMore About AnswersNowAnswersNow welcomes applicants of all backgrounds, experiences, and abilities. We believe a diverse team is a strong team, and are committed to provide a fair and equitable experience for every candidate. If you require reasonable accommodations at any stage, we encourage you to reach out. We’re here to support!Learn more about us at getanswersnow.com.