About Us
Kardigan is a heart health company working to make cardiovascular disease preventable, curable and no longer the leading cause of death in the world.
It is Kardigan’s mission to develop multiple targeted treatments in parallel that bring people with cardiovascular diseases to the cures they deserve.
Led by Tassos Gianakakos, Jay Edelberg, M.D., Ph.D., and Bob McDowell, Ph.D., Kardigan’s co-founders have reunited after leading MyoKardia to discover and develop mavacamten, the first cardiac myosin inhibitor, resulting in an acquisition by Bristol Myers Squibb in 2020.
We have a cutting-edge discovery and translational research platform, a pipeline of late-stage candidates, and an industry-leading team that is driven to improve the lives of patients.
At Kardigan, we are motivated by our values which guide how we work, interact, and achieve our goals. Driven by patients and their families, we are deeply committed to improving the lives of patients and prioritizing their needs above all else. We believe in being authentic—leading with truth to bring out the best in others by creating an environment where every person knows they will be fully accepted. With an eagerness to learn, we encourage the highest levels of curiosity and are open to changing our minds. We are committed to winning as a team with urgency, excellence, and intention, and support each other no matter what role we play or where we sit. Lastly, we strive to enable the impossible because patients are counting on us. We are not afraid to take risks to unlock innovation and advance scientific discoveries.
These values are the foundation of our work, empowering us to make a real difference, every day.
About the Team
Kardigan's Information Technology team is at the forefront of enabling innovation across the enterprise. We partner closely with colleagues throughout the entire enterprise to deliver secure, reliable, and forward-looking technology solutions that accelerate execution and unlock the power of data and artificial intelligence.
Our team is passionate about solving complex business challenges through technology. We foster a culture of collaboration, ownership, continuous improvement, and curiosity, where team members are empowered to introduce new ideas, modernize ways of working, and help shape the future of a growing biotechnology company. If you are excited by the opportunity to build, innovate, and make a tangible impact, you'll find a rewarding environment at Kardigan.
Main Responsibilities
The Associate Director / Director, Data and AI Solution Development is a solution-design leader responsible for defining and shaping Kardigan's enterprise data and AI solutions — translating business, scientific, and operational needs into well-architected solution designs, data models, and integration patterns. This role owns solution architecture, requirements analysis, and design for initiatives spanning data products, enterprise data management, and AI-enabled applications, and partners closely with the Solution Delivery team, which owns build and deployment, and the Data Platform Engineering team, which owns the underlying platform's build and operations.
A core focus of this role is designing solutions that make enterprise data usable as high-quality context for human and AI — defining the requirements and designs that ensure data feeding LLMs, RAG systems, analytics copilots, and other AI/ML applications is accurate, well-governed, and discoverable, so that partner teams can build and operationalize it with confidence. More broadly, this role is a driver of Kardigan's technology-enabled transformation, helping the organization innovate through technology and treat data and AI as strategic assets rather than cost centers. They will shape how emerging data and AI capabilities translate into real competitive advantage — accelerating R&D, improving decision-making, and differentiating Kardigan as a modern, data-driven biopharma company.
This individual will operate primarily as a solution architect and designer: setting solution architecture standards, running requirements analysis and design workshops, and partnering with business and scientific stakeholders to shape data and AI solutions. Execution and delivery are owned by a dedicated Solution Delivery team, and platform build/operations are owned by Data Platform Engineering; this role is the design authority that bridges the two, reporting to the Executive Director overseeing Data, AI & Technology and serving as a strategic peer to Data Science/Analytics and AI leadership.
Enterprise Solution Architecture & Design
- Lead solution architecture and design for enterprise data and AI initiatives — translating business and scientific requirements into technical solution designs, data models, and integration patterns
- Define solution patterns, reference architectures, and reusable templates for data and AI solutions that the Solution Delivery team and Data Platform Engineering can build from
- Own requirements definition and business analysis for complex, cross-functional data and AI initiatives, partnering closely with business and scientific stakeholders to clarify and document needs
- Develop proof-of-concepts, prototypes, and solution blueprints to validate approaches and de-risk designs before handoff to the Solution Delivery team; Define acceptance criteria and solution success measures that the Solution Delivery team uses to validate what is built
- Own and prioritize the enterprise data and AI solutions roadmap and intake process, in partnership with business, scientific, and technology stakeholders
Data & AI Solution Foundations
- Partner with the Data Platform Engineering team and Enterprise Architecture team to define the data, API, and platform requirements needed to support new solution designs, including data APIs, semantic interfaces, and access patterns for both human users and AI agents
- Define solution-level requirements for the semantic layer — governed metric definitions, lineage, and natural-language access patterns — so that human users and AI agents can reliably consume enterprise data
- Advise on where the enterprise data warehouse/lakehouse, orchestration layers, and streaming infrastructure need to evolve to support new solution designs, partnering with the platform engineering function that owns their build and operation
- Define requirements for low-latency and near-real-time data access needed to support time-sensitive solution use cases
- Evaluate emerging data and AI tooling (e.g., MCP servers, vector databases, low-code/no-code platforms) for fit with upcoming solution designs, in partnership with Data Platform Engineering
Enterprise Data Management
- Define enterprise data management requirements and standards — data governance, master data management (MDM), metadata management, cataloging, and lineage
- Define and enforce enterprise data standards, taxonomies, and ontologies that drive data quality, consistency, and discoverability across solutions
- Partner with functional data owners (R&D, Clinical, Commercial, Finance, G&A) and data stewardship programs to ensure solution designs reflect accountability for data quality and integrity
- Ensure solution designs comply with applicable regulatory and public-company requirements, including FDA 21 CFR Part 11, data integrity (ALCOA+) principles, GDPR/HIPAA as applicable, and SOX-relevant data controls appropriate for a newly public company
- Partner with Legal, Regulatory Affairs, Quality, and Internal Audit to embed data integrity, privacy, and access-control requirements into every data and AI solution design
Data Products & Solutions-as-a-Service Design
- Define the vision and design for enterprise data products — curated, documented, reusable datasets and APIs — delivered as a service to business, scientific, and technology teams
- Design the solution and data product intake model (SLAs, catalog structure, self-service access patterns) that the Solution Delivery team operationalizes
- Partner with business and scientific stakeholders to translate requirements into well-architected data and AI solution designs, dashboards, and analytics-ready data specifications
- Define solution and data product quality, adoption, and value metrics, and use them to continuously refine solution designs
Agentic Analytics & AI-Facing Solution Design
- Design solutions that enable AI agents to draft queries, detect anomalies, generate insights, and surface recommendations — shifting the organization from dashboard review toward proactive, AI-assisted intelligence
- Define solution designs for AI/ML workloads — including model training data, feature serving, and RAG retrieval — not only dashboards and reports
- Apply hands-on understanding of how LLMs and AI agents consume data — semantic layers, embeddings, vector search, and tool-use patterns (MCP, function calling) — to solution design decisions
- Evaluate and recommend AI-assisted tooling (e.g., automated data quality checks, natural-language-to-SQL) for incorporation into future solution designs
Preparing Data as Context for AI Solutions
- Design the data foundation that enables enterprise AI solutions — including RAG, AI agents, and LLM applications — defining requirements that ensure source data is clean, current, well-structured, and appropriately governed before it is used as AI context
- Architect knowledge base, vector store/embeddings, and semantic layer designs that make enterprise data reliably retrievable and usable by AI-enabled solutions
- Partner with the AI/data science function to define requirements for data pipelines that feed AI/ML models and generative AI solutions, including requirements for monitoring data drift, freshness, and quality
- Define data access control, redaction/masking, and audit logging requirements for data used in AI contexts to maintain compliance, privacy, and responsible-AI standards
- Define documentation and validation requirements for AI data solutions appropriate to a regulated, GxP-adjacent environment
Leadership & Organizational Impact
- Own and unify the data and AI solution design roadmap across solution architecture, data management, data products, and AI-enablement workstreams, serve as the primary point of accountability for data and AI solution design
- Champion a strong solution design culture grounded in requirements discipline, architecture rigor, and reusability, and advise on vendor and tooling selection in partnership with procurement and delivery teams
- Define and report on KPIs/OKRs for solution design quality, requirements clarity, reuse of solution patterns, and stakeholder satisfaction
- Collaborate closely with IT Operations, Solution Delivery, Data Platform Engineering, Cybersecurity, and Enterprise Architecture peers to ensure solution designs are secure, well-integrated, and architecturally coherent with the broader IT landscape
About You
Basic Qualifications
- Bachelor's degree required in Computer Science, Data Engineering, Information Systems, Engineering, or a related quantitative discipline. Advanced degree (M.S. or MBA) preferred.
- Proven track record designing enterprise data and AI solutions — solution architecture, data models, integration patterns — at enterprise scale within a regulated industry
- Deep technical depth in data architecture, solution design, and cloud platforms, balanced with the ability to operate strategically; working proficiency with SQL and Python to evaluate and validate solution designs
- Strong business analysis and requirements-definition skills, with experience translating ambiguous business and scientific requirements into technical solution designs
- Working knowledge of cloud-based data platforms (e.g., Snowflake, Databricks, AWS, Azure, or GCP data services) sufficient to design solutions that partner teams can build and operate
- Demonstrated experience designing data models and integration patterns connecting enterprise systems (e.g., ERP, CRM, LIMS/ELN, clinical systems) to analytics and AI platforms
- Familiarity with modern data stack components: warehouse infrastructure, streaming ingestion, semantic/metrics layers, governance and cataloging tools, and orchestration frameworks
- Experience defining data governance, MDM, or metadata management requirements in a regulated or enterprise environment
- Practical experience designing data solutions for AI/ML or generative AI use cases (e.g., RAG pipelines, feature stores, embeddings/vector databases)
- Demonstrated success partnering with Data Science/Analytics leadership as a peer stakeholder
- Excellent cross-functional communication skills, with experience presenting solution vision and architecture to executive stakeholders
- Track record of operating effectively in fast-paced, lean, resource-constrained organizations as a hands-on solution architect and designer
- Strong product and solution mindset — treats data and solutions as products and services, not just projects
- Clear, structured communicator able to translate technical concepts for both engineers and business/scientific stakeholders
- Entrepreneurial mindset with high tolerance for ambiguity and a bias for action
- Strong business acumen — understands how data and AI solution investments connect to pipeline value, competitive differentiation, and patient outcomes
- Unwavering commitment to responsible AI, data ethics, patient privacy, and regulatory compliance
Strongly Preferred Experience
- 5 - 7 years of experience in data engineering, solution architecture, or enterprise data/AI solution design.
- Experience in life sciences, biotechnology, or pharmaceutical environments, including familiarity with clinical, regulatory, or scientific data types
- Familiarity with GxP data integrity requirements (21 CFR Part 11, ALCOA+) and public-company data controls (SOX)
- Experience with data cataloging and governance tooling (e.g., Collibra, Alation, Purview, Unity Catalog)
- Hands-on familiarity with generative AI platforms (Azure OpenAI Service, AWS Bedrock, Anthropic Claude API) and validated deployment in a regulated environment
- Hands-on understanding of how LLMs and AI agents consume data, including semantic layers, embeddings, vector search, and tool-use patterns (MCP, function calling)
- Track record designing data-as-a-product solutions — APIs and self-service platforms where internal teams and functional partners are the primary consumers
- Familiarity with enterprise architecture frameworks (e.g., TOGAF) and enterprise integration patterns
- Experience partnering with delivery teams operating under SDLC or Agile/Scrum methodologies to hand off solution designs for execution
Full Salary Range
Base salary range: $175,000 – $250,000 USD, plus annual bonus and equity, commensurate with experience and location.
Kardigan is an Equal Opportunity Employer. We are committed to creating a diverse and inclusive workplace and consider all qualified applicants for employment without regard to race, color, religion, sex (including pregnancy, childbirth, breastfeeding, gender identity, gender expression, and sexual orientation), national origin, age, disability, genetic information, veteran status, marital status, citizenship status, or any other characteristic protected by applicable federal, state, or local law. We are committed to providing reasonable accommodations to qualified individuals with disabilities throughout the application and employment process.
