Lead the development of analytics infrastructure for a clinical AI/ML product at an early-stage healthcare startup.
Posted by employer 9 months ago
First seen on Joblaze 1 month ago
Last verified on the company career page 1 day ago
Requirements
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In this role, the Software Engineer for Analytics at Bayesian Health will focus on developing the analytics infrastructure and tools that support clinical AI/ML products, enhancing decision-making through data insights. Key skills include proficiency in Python and SQL, along with experience in building scalable analytics solutions on cloud platforms like AWS. This position is ideal for a senior engineer with a background in healthcare data analytics, who thrives in a collaborative, fast-paced startup environment. Bayesian Health's diverse team is dedicated to improving patient outcomes through innovative data solutions.
Joblaze insights
Quick facts
From the original posting
We’re an early-stage startup on a mission to make healthcare proactive by empowering physicians, nurses, and care team members with real-time data to save lives.
You will lead the development of our clinical AI/ML product analytics infrastructure, frameworks, and tools to enable our client success, product, clinical, and technology teams to uplevel our decision making through better insights.
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.
As a Senior Software Engineer, Analytics, you will work closely with client success, product managers, clinicians, data scientists, and other software engineers to build infrastructure, frameworks, and tools to improve client analytics, facilitate clinical case reviews, and support product investigation. This role is crucial to provide internal visibility into product performance that will drive expansion of our clinical AI/ML module offerings and revenue growth.
Responsibilities
Product performance monitoring and optimization: Partner with client success and clinical product subject matter experts to implement the infrastructure, queries, and automation to monitor the KPIs and success metrics of our products across multiple clinical domains and clients.
Clinical case review and investigation: Build frameworks and tools to empower our clinical team to independently review and investigate clinical cases reported by our clients and identify cases with certain criteria that need further evaluation.
Data platform and analytics technical foundations: Propose and implement foundational improvements and innovations to boost our data platform scalability with expanding products and clients and uplevel our team analytics capabilities.
Drive product analytics development cross-functionally: Work closely with Client Success, Clinical, Product, Data Science, and Engineering to drive alignment on product analytics at the company level.
BS in Computer Science or other relevant technical discipline.
5+ years of experience in building scalable, secure analytics infrastructure and tools on a cloud platform (preferably AWS) to produce monitoring metrics and investigational data from complex data models and queries for 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 working with sensitive data that contains PHI/PII.
Excellent communication skills and a proven ability to collaborate with cross-functional teams (data science, product, clinical) to translate requirements into robust technical solutions
Experience in leveraging LLMs in distributed data processing and analytics systems.
Experience building analytics technology for clinical/health data.
Experience handling ambiguity and uncertainty in a startup.
Standard company text repeated across Bayesian Health's postings is omitted here.