Applied Scientist III
Build and deploy algorithmic systems for high-impact healthcare problems, choosing among machine learning, optimization, heuristics, and hybrid approaches. The role requires 4+ years of relevant industry experience, strong applied problem-solving and evaluation skills, and fluency in modern ML tooling.
$236k – $260k/yr
On-site4+ YOEML Engineering
About the job
Responsibilities
- Design, develop, deploy, and improve algorithmic systems powering healthcare products.
- Own ambiguous, high-stakes problems end to end, from problem framing and objective-function definition through production deployment and iteration.
- Translate healthcare and business constraints into clear objectives, tradeoffs, and decision frameworks.
- Define metrics and validate solutions before release.
- Select appropriate approaches, including machine learning, optimization, heuristics, expert systems, and hybrid methods.
- Develop novel solutions and prototypes for complex problems.
- Review technical work rigorously and establish quality standards and evaluation tooling.
- Build a deep understanding of the healthcare economy and the company's role within it.
Example initiatives
- Optimize provider tiering across geographic access and total cost of care.
- Fine-tune and productionize an LLM-based primary care experience, including evaluation, guardrails, and quality monitoring.
- Build member engagement models using claims and in-app behavior to select effective communication channels and timing.
Requirements
- 4+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 2+ years with a relevant advanced degree.
- PhD preferred.
- Strong applied problem-solving skills and ability to define and improve meaningful metrics.
- Fluency across data and algorithmic methods.
- Strong judgment in selecting statistical models, heuristics, optimization, and simpler algorithmic approaches.
- Strong communication skills, including explaining complex algorithmic ideas to senior and external stakeholders and securing cross-team alignment.
- Bias toward action and ability to rapidly turn ideas into working prototypes.
Technologies
- Python
- SQL
- AWS
- Snowflake
- pandas
- XGBoost
- PyTorch
- Hugging Face
- Modern LLM tooling and evaluation frameworks
Compensation and Benefits
- Target base compensation: $236,000–$260,000.
- Equity incentive plan.
- Flexible paid time off.
- Medical, dental, and vision plan options.
- 401(k) with company match.
- Flexible spending accounts.
- Teladoc Health.
Skills
PythonSQLAWSSnowflakepandasXgboostPyTorchHugging FaceLLMsMachine LearningOptimizationHeuristicsClaims DataEvaluation Frameworks