ABOUT SAIVA AI SAIVA AI applies machine learning to make optimal use of electronic health data for the most vulnerable healthcare population. Our mission is to improve patient outcomes by augmenting clinical decision-making with the power of AI. Based in Silicon Valley, our team is a group of passionate healthcare technology veterans, engineers, and data scientists, including from Stanford University, leveraging cutting edge technology to predict patient risk and provide tools that drive timely intervention. POSITION DESCRIPTION As a data scientist, you will own the correctness of the SQL that powers our client portal KPIs and run causal impact analyses that connect product use to patient outcomes. Expect varied, investigative work across structured + unstructured clinical data- with others handling back-end, front-end, and production query optimization. RESPONSIBILITIES In this position you will: Be the source of truth for metric definitions & SQL: numerators/denominators, cohort windows, timezone handling, late-arriving data, backfills, and window functions across multi-table joins. Analyze product utilization vs outcomes (e.g., report opens vs rehospitalization windows), choose appropriate causal designs, and explain results and assumptions clearly. Compare best vs worst model performance across buildings/cohorts; propose actionable fixes to lift recall/precision where it lags. Work hands-on in SQL + Python (R acceptable), using notebooks for analyses; BI tool familiarity is a plus (not a deliverable). Requirements ~2–4 years in industry as a data scientist/econometrician/biostatistician with solid SQL and causal chops. Evidence of owning multi-table SQL with windowing and time logic, and of shipping causal/observational studies that influenced decisions. Comfortable selecting methods that fit the data (e.g., experiments when possible; otherwise event studies/DiD, matching/weighting, survival). Undergrad degree required in Econometrics, Statistics, or a closely related quantitative field (CS minor or equivalent skills a plus). Nice to have: prior healthcare outcomes work or first-author publication experience.
ABOUT SAIVA AI SAIVA AI applies machine learning to make optimal use of electronic health data for the most vulnerable healthcare population. Our mission is to improve patient outcomes by augmenting clinical decision-making with the power of AI. Based in Silicon Valley, our team is a group of passionate healthcare technology veterans, engineers, and data scientists, including from Stanford University, leveraging cutting edge technology to predict patient risk and provide tools that drive timely intervention. POSITION DESCRIPTION As an Applied ML Scientist, you will use your extensive experience to build and improve models across structured + unstructured clinical data, pairing boosting models (like xgboost) with language models (LLM APIs, smaller Transformers you will fine tune or train) to lift real-world impact. You’ll prototype fast, run rigorous evaluations, and deliver sound research code that our MLEs will harden & optimize and our MLOps will ship & monitor. RESPONSIBILITIES In this position you will: For a given task, ship a prompted LLM baseline → fine-tuned small LM or BERT-class model, comparing precision@recall, cost, and latency. Build light RAG/explainability: return the strongest supporting sentence/span from notes that justifies the classification. Leverage domain NER (don’t rebuild it) to target specific acute phenotypes and improve downstream classifiers. Distill/quantize to hit latency/cost SLOs while preserving recall; hand off clean checkpoints/configs/eval harnesses. Work hands-on in Python, Jupyter, Git, SQL, MLflow; AWS (EC2/ECS/S3) or equivalent cloud. Requirements 2+ years in industry doing hands-on NLP (IC), across prompting, fine-tuning, and training BERT-class models. Evidence of high-recall use cases with calibration and defensible thresholds; comfort with noisy note text. Undergrad degree required in Computer Science or Computational Linguistics (strong quantitative background). Nice to have: visible healthcare NLP work (papers, preprints, GitHub).
ABOUT SAIVA AI SAIVA AI applies machine learning to make optimal use of electronic health data for the most vulnerable healthcare population. Our mission is to improve patient outcomes by augmenting clinical decision-making with the power of AI. Based in Silicon Valley, our team is a group of passionate healthcare technology veterans, engineers, and data scientists, including from Stanford University, leveraging cutting edge technology to predict patient risk and provide tools that drive timely intervention. POSITION DESCRIPTION As a Data Scientist, you will own the correctness of the SQL that powers our client portal KPIs and run causal impact analyses that connect product use to patient outcomes. Expect varied, investigative work across structured and unstructured clinical data with others handling back-end, front-end, and production query optimization. RESPONSIBILITIES In this position you will: Be the source of truth for metric definitions & SQL: numerators/denominators, cohort windows, timezone handling, late-arriving data, backfills, and window functions across multi-table joins. Analyze product utilization vs outcomes, choose appropriate causal designs, and explain results and assumptions clearly. Compare best vs worst model performance. Work hands-on in SQL + Python, using notebooks for analyses; BI tool familiarity is a plus. REQUIREMENTS 2–4 years in industry as a data scientist/econometrician/biostatistician with solid SQL and causal chops. Evidence of owning multi-table SQL with windowing and time logic, and of shipping causal/observational studies that influenced decisions. Comfortable selecting methods that fit the data (e.g., experiments when possible; otherwise event studies/DiD, matching/weighting, survival). Undergrad degree required in Econometrics, Statistics, or a closely related quantitative field (CS minor or equivalent skills a plus). Nice to have: prior healthcare or outcomes work.
ABOUT SAIVA AI SAIVA AI applies machine learning to make optimal use of electronic health data for the most vulnerable healthcare population. Our mission is to improve patient outcomes by augmenting clinical decision-making with the power of AI. Based in Silicon Valley, our team is a group of passionate healthcare technology veterans, engineers, and data scientists, including from Stanford University, leveraging cutting edge technology to predict patient risk and provide tools that drive timely intervention. POSITION DESCRIPTION As a Senior Applied ML Scientist, you will build and improve models across structured and unstructured clinical data, pairing boosting models with language models to lift real-world impact. You’ll prototype fast, run rigorous evaluations, and deliver sound research code that our MLEs will harden and optimize and our MLOps will ship and monitor. RESPONSIBILITIES In this position you will: Develop or enhance high-dimensional boosting models; feature engineer, calibrate, and improve recall or precision on imbalanced data. Prototype with LLMs; distill/quantize them to meet latency + cost SLOs Own evaluation: offline backtests, cohort/threshold analysis, calibration, decision curves; document and communicate trade-offs clearly. Collaborate with MLEs for serving; provide clean correct artifacts (checkpoints, configs, eval harnesses). Work hands-on in Python, Jupyter, SQL, Git, MLflow on AWS (S3/EC2/ECS/CPUs/GPUs) REQUIREMENTS 4+ years post-undergrad industry experience as a hands-on IC focusing on MLR. Track record with boosting + Transformers/LLMs (API prototyping and smaller-model fine-tuning). Evidence you’ve hit accuracy + cost/latency targets at scale. Comfortable proposing new modeling approaches and new prediction targets. Undergrad degree required in Computer Science, Data Science, Statistics, Econometrics, Physics, Applied Math, or Electrical Engineering. Nice to have: prior healthcare or outcomes work.
ABOUT SAIVA AI SAIVA AI applies machine learning to make optimal use of electronic health data for the most vulnerable healthcare population. Our mission is to improve patient outcomes by augmenting clinical decision-making with the power of AI. Based in Silicon Valley, our team is a group of passionate healthcare technology veterans, engineers, and data scientists, including from Stanford University, leveraging cutting edge technology to predict patient risk and provide tools that drive timely intervention. POSITION DESCRIPTION As a Senior Applied ML Scientist, you will build and improve models across structured and unstructured clinical data, pairing boosting models with language models to lift real-world impact. You'll prototype fast, run rigorous evaluations, and deliver sound research code that our MLEs will harden and optimize and our MLOps will ship and monitor. RESPONSIBILITIES In this position you will: Develop or enhance high-dimensional boosting models; feature engineer, calibrate, and improve recall or precision on imbalanced data. Prototype with LLMs; distill/quantize them to meet latency + cost SLOs Own evaluation: offline backtests, cohort/threshold analysis, calibration, decision curves; document and communicate trade-offs clearly. Collaborate with MLEs for serving; provide clean correct artifacts (checkpoints, configs, eval harnesses). Work hands-on in Python, Jupyter, SQL, Git, MLflow on AWS (S3/EC2/ECS/CPUs/GPUs) REQUIREMENTS 5+ years post-undergrad industry experience as a hands-on IC focusing on MLR. Track record with boosting + Transformers/LLMs (API prototyping and smaller-model fine-tuning). Evidence you've hit accuracy + cost/latency targets at scale. Comfortable proposing new modeling approaches and new prediction targets. Undergrad degree required in Computer Science, Data Science, Statistics, Econometrics, Physics, Applied Math, or Electrical Engineering. Nice to have: prior healthcare or outcomes work.
ABOUT SAIVA AI SAIVA AI applies machine learning to make optimal use of electronic health data for the most vulnerable healthcare population. Our mission is to improve patient outcomes by augmenting clinical decision-making with the power of AI. Based in Silicon Valley, our team is a group of passionate healthcare technology veterans, engineers, and data scientists, including from Stanford University, leveraging cutting edge technology to predict patient risk and provide tools that drive timely intervention. POSITION DESCRIPTION As a Data Scientist, you will own the correctness of the SQL that powers our client portal KPIs and run causal impact analyses that connect product use to patient outcomes. Expect varied, investigative work across structured and unstructured clinical data with others handling back-end, front-end, and production query optimization. RESPONSIBILITIES In this position you will: Be the source of truth for metric definitions & SQL: numerators/denominators, cohort windows, timezone handling, late-arriving data, backfills, and window functions across multi-table joins. Analyze product utilization vs outcomes, choose appropriate causal designs, and explain results and assumptions clearly. Compare best vs worst model performance. Work hands-on in SQL + Python, using notebooks for analyses; BI tool familiarity is a plus. REQUIREMENTS 24 years in industry as a data scientist/econometrician/biostatistician with solid SQL and causal chops. Evidence of owning multi-table SQL with windowing and time logic, and of shipping causal/observational studies that influenced decisions. Comfortable selecting methods that fit the data (e.g., experiments when possible; otherwise event studies/DiD, matching/weighting, survival). Undergrad degree required in Econometrics, Statistics, or a closely related quantitative field (CS minor or equivalent skills a plus). Nice to have: prior healthcare or outcomes work.