Data Scientist

AnswersNow Inc•,

26d•$144,000 - $168,000•Remote

About The Position

At 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.

Requirements

4+ years of experience in applied data science, ML engineering, or AI engineering in a production environment
Deep understanding of RAG architectures: retrieval systems, embedding models, vector databases (Pinecone, Weaviate, pgvector, or similar), chunking strategies, and context assembly
Experience designing and running evaluation frameworks for AI systems — you've thought hard about how to measure quality in domains where ground truth is ambiguous
Strong 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 design
You think like an engineer and a scientist: you build systems that can be measured, iterated on, and trusted — not black boxes
Strong written communication: you can explain RAG pipeline design to a clinician and explain clinical requirements to an engineer
Genuine interest in the clinical domain — you want to understand Applied Behavior Analysis well enough to build AI that actually helps BCBAs do their jobs

Nice To Haves

Experience with Amazon Bedrock, SageMaker, or AWS AI/ML services
Familiarity with HIPAA-compliant data handling for AI training and inference pipelines
Background in clinical NLP, behavioral health informatics, or ABA/autism research
Experience with fine-tuning or RLHF — even if this role doesn't require it, understanding the tradeoffs informs better RAG design
Exposure to LLM-as-judge evaluation patterns or multi-model evaluation pipelines

Responsibilities

Architect 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 prompts
Design the retrieval layer: chunking strategies, embedding models, vector store configuration, and retrieval ranking — optimizing for clinical relevance, not just semantic similarity
Build a context assembly system that selects and structures the most relevant clinical information for each model invocation, given token constraints and clinical priority
Evaluate retrieval quality rigorously: build test sets, measure recall and precision, and iterate on the pipeline based on where retrieval fails
Design evaluation frameworks that assess AI recommendation quality beyond standard NLP metrics — working with clinical stakeholders to define what 'good' means for each use case
Build automated evaluation pipelines that can test AI outputs at scale: LLM-as-judge evaluators, human review workflows, and clinical validity checks
Maintain evaluation datasets that reflect the real distribution of clinical scenarios the model encounters in production
Report evaluation results in terms that clinical and product stakeholders can understand and act on
Systematically 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 handle
For each identified gap, recommend and implement the appropriate mitigation — improved retrieval, prompt engineering, output validation, or escalation to human review
Stay current on foundation model capabilities and evaluate new models against our clinical requirements as they emerge
Maintain a gap log and roadmap that gives product and clinical leadership visibility into current AI limitations and the plan to address them
Monitor production AI outputs for quality, drift, and failure modes using the evaluation infrastructure you've built
Define alerting thresholds and escalation paths for when AI quality falls below acceptable clinical standards
Partner with the engineering team on observability — ensuring AI outputs are logged, traceable, and auditable
Conduct root-cause analysis when AI quality issues are reported and drive systematic fixes
Work closely with clinical leadership and BCBAs to understand the care model deeply enough to design AI systems that support it accurately
Translate 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 clinician
Partner with the BI Engineer and data team on the data infrastructure that feeds the AI pipeline — session data, outcomes data, treatment plan content
Communicate AI system behavior clearly to non-technical stakeholders: what the system does, what it doesn't do, and where human judgment remains essential