Data Scientist

About Hello Heart

Hello Heart is on a mission to change the way people care for their hearts. The company provides the first app and connected heart monitor to help people track and manage their heart health. With Hello Heart, users take steps to control their risk of heart attacks and stroke – the leading cause of death in the United States. Peer-reviewed studies have shown that high-risk users of Hello Heart have seen meaningful drops in blood pressure, cholesterol and even weight. Recognized as the digital leader in preventive heart health, Hello Heart is trusted by more than 130 leading Fortune 500 and government employers, national health plans, and labor organizations. Founded in 2013, Hello Heart has raised more than $138 million from top venture firms and is a best-in-class solution on the American Heart Association’s Innovators’ Network and CVS Health Point Solutions Management platform. Visit www.helloheart.com for more information.

About The Role

Hello Heart is seeking a talented and mission-driven Data Scientist to join our growing Data organization. As a Data Scientist, you will own complex, high-impact projects end-to-end from research and experimentation through production deployment and monitoring.

You will develop and refine the machine learning models that power our personalization engine, engagement optimization, and clinical insights. Your work will directly shape the way millions of people experience their healthcare and improve their well-being.

Responsibilities

• Lead end-to-end development of machine learning models, from research, data exploration, and feature engineering to training, validation, deployment, and post-production monitoring.
• Partner closely with product managers, data engineers, and software engineers to translate strategic questions and user behavior patterns into measurable, data-driven solutions.
• Research, prototype, and implement cutting-edge ML, deep learning, causal inference, and reinforcement learning techniques to tackle complex healthcare challenges.
• Contribute to ML infrastructure and tooling to ensure scalability, reliability, reproducibility, and performance across all production workflows.
• Drive innovation by exploring new approaches in Generative AI, LLM-based systems, and AI-agent architectures to enhance recommendation, engagement, and personalization capabilities.
• Design, execute, and interpret A/B tests and other experimental methodologies to rigorously measure the impact of new models, product features, and interventions.
• Collaborate, mentor, and share best practices to elevate technical excellence across the Data Science discipline.
  
  

• 3+ years of hands-on experience developing, deploying, and maintaining ML models in production environments.
• Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field.
• Strong proficiency in Python, including experience with production-grade code practices, version control, testing, and reproducibility.
• Deep understanding of statistics, probability, causal inference, and experimental design (e.g., hypothesis testing, A/B testing).
• Expertise with ML techniques such as supervised and unsupervised learning, neural networks, clustering, regression/classification models, and causal modeling.
• Experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, XGBoost, or LightGBM.
• Strong analytical thinking and the ability to clearly translate data-driven insights into actionable business and product recommendations.
• Excellent collaboration skills and the ability to work cross-functionally with engineering, product, clinical, and other stakeholders.
  
  

• Experience or strong interest in Generative AI, LLMs, or AI agent systems.
• Familiarity with recommendation systems, reinforcement learning, or advanced causal inference.
• Knowledge of cloud platforms (AWS, GCP, Azure), containerization tools (Docker, Kubernetes), and MLOps pipelines.
• Experience working with healthcare or clinical datasets, including EMR or claims data.