Software Engineer, Data & Analytics Platform
Bayesian Health, Inc.
About the role
Software Engineer, Data & Analytics Platform
In Brief
We’re a rapidly growing startup on a mission to make healthcare proactive by empowering physicians, nurses, and care team members with real-time data to save lives.
We’re looking for a software engineer to join the team to build and scale our data and analytical platform that tells us and our clients how our clinical AI products are performing in every hospital we serve. You’ll play an important role in shaping key technical decisions, building the data infrastructure our company and clients rely on, and see your work make a real difference in patient care.
Who We Are
Bayesian Health’s mission is to improve patient outcomes by empowering clinicians with the insights they need to make the right decision for the right patient at the point-of-care. We’re a diverse team of clinicians, engineers, machine learning experts, product designers, and performance improvement leaders committed to enabling smarter, patient-specific care delivery through unlocking the power of data.
We’re funded by top tier tech and biotech investors: Obvious Ventures, Andreessen Horowitz, American Medical Association’s venture arm, Catalio Partners, and LifeForce Capital. Our company has won many awards; most recent recognitions include: Forbes AI Top 50, World Economic Forum Tech Pioneer, Time Best Inventions, BioTech AI Company of the Year.
Read more about our recent publication in Nature Medicine that associates our products with lives saved.
What you’ll do
You’ll work closely with other engineers, data scientists, clinicians, product managers, and client success partners to turn complex clinical data into reliable, actionable insights. Day to day, that means designing pipelines and data models, building tools that help our teams monitor product performance and investigate clinical cases, and improving the platform as we expand to new products and health systems. Your work will give the company clear visibility into how our AI performs, which directly shapes how we grow our clinical AI offerings and support our clients.
Responsibilities
Build the platform's foundations. Develop our data lakehouse and warehouse, with secure, scalable pipelines that bring our data together to power analytics and machine learning. As our products and client base grow, you'll drive improvements in data modeling, orchestration, data quality, and scalability.
Enable self-serve insights with LLMs and automation. Create tools and frameworks that let our Clinical, Client Success, and Product teams get answers from our data without writing queries, from reviewing and flagging clinical cases to uncovering trends and opportunities.
Power performance monitoring for our clinical AI. Design pipelines and data models that automatically track KPIs and model performance across every client and clinical module we support, using real-time EHR data that is messy and heterogeneous.
Set the standard for analytics company-wide. Work with Client Success, Clinical, Product, Data Science, and Engineering to define how we measure product success and make sure the data behind those measures is trustworthy.
Minimum qualifications
BS in Computer Science or other relevant technical discipline.
5+ years of experience in building scalable, secure data or analytics infrastructure on a cloud platform (preferably AWS), serving live products and customers.
Proficient in Python and SQL.
Deep knowledge in modern data and analytics technologies, such as cloud-based data warehouses, transformation frameworks (e.g. dbt), workflow orchestration tools, and BI tools like Tableau or Quicksight, and keen ability to integrate with existing infrastructure to enhance capabilities.
Experience in leveraging LLMs in distributed data processing and analytics systems.
Excellent communication skills and a proven ability to collaborate with cross-functional teams (data science, product, clinical) to translate requirements into robust technical solutions
Preferred qualifications
Experience handling sensitive data that contains PHI/PII securely.
Experience building analytics technology for clinical or health data.
Experience handling ambiguity and uncertainty in a startup.
Bayesian Health provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
Responsibilities
- Build and scale secure data lakehouse and warehouse infrastructure, pipelines, and data models.
- Create tools that enable teams to explore data and investigate clinical cases using LLMs and automation.
- Develop pipelines and models to monitor clinical AI performance and KPIs across clients and products.
- Collaborate cross-functionally to define product success metrics and ensure trustworthy data.
Qualifications
- Bachelor’s degree in Computer Science or a related technical discipline.
- 5+ years building scalable, secure data or analytics infrastructure on a cloud platform, preferably AWS.
- Proficiency in Python and SQL.
- Knowledge of cloud data warehouses, dbt or similar transformation frameworks, workflow orchestration, and BI tools.
- Experience leveraging LLMs in distributed data processing and analytics systems.
- Strong communication and cross-functional collaboration skills.
Skills mentioned
About Bayesian Health, Inc.
Bayesian Health is the real-time clinical intelligence platform that helps health systems deliver proactive, high-reliability care. By continuously reading the full patient record to establish each patient's baseline and detect meaningful change over time, Bayesian applies complex clinical reasoning to identify the patients who truly need attention and surface clear next steps inside the EHR — turning data noise into trusted, decisive action. Leading health systems partner with Bayesian to make the long-promised shift from reactive to proactive care across key drivers of mortality, readmissions, and length of stay, hardwiring quality as a durable strategy rather than a series of disconnected initiatives.