ROLE RESPONSIBILITIES
- Lead deployment of production AI solutions and reusable software components with automated self-monitoring QA/QC processes
- Implement QA and testing, data ops, model ops, and DevOps for data science workflow products, industrialized workflow accelerators, and best practices in the production deployment of scalable AI/ML analytic insights products
- Enforce best practices for QA and testing and SDLC production support to ensure reliability and availability of deployed software
- Act as a subject matter expert for production deployment processes of data science workflows, AI solutions, and reusable software components on cross functional teams in bespoke organizational initiatives by providing thought leadership and execution support
- Direct QA and testing, data ops and model ops, DevOps, platform and cloud engineering research, advance data science workflow CI/CD orchestration capabilities, drive improvements in automation and self-service production deployment processes, implement best practices, and contribute to the broader talent building framework by facilitating related trainings
- Set a vision, prioritize workstreams, and provide day-to-day leadership, supervision, and mentorship for a global team with technical & functional expertise that includes QA and testing, DevOps, data science, and operations
- Coach direct reports to adopt best practices, improve technical skills, develop an innovative mindset, and achieve professional growth through technical and organizational thought leadership
- Communicate value delivered through reusable AI components to end user functions (e.g., Chief Marketing Office, Biopharma Commercial and Medical Affairs) and evangelize innovative ideas of reusable & scalable development approaches/frameworks/methodologies to enable new ways of developing and deploying AI solutions
- Partner with other leaders within the Data Science Industrialization team to define team roadmap and drive impact by providing strategic and technical input including platform evolution, vendor scan, and new capability development
- Partner with AI use case development teams to ensure successful integration of reusable components into production AI solutions
- Partner with AIDA Platforms team on end to end capability integration between enterprise platforms and internally developed reusable component accelerators (API registry, ML library / workflow management, enterprise connectors)
- Partner with AIDA Platforms team to define best practices for production deployment of reusable components to identify and mitigate potential risks related to component performance, security, responsible AI, and resource utilization
BASIC QUALIFICATIONS
- Bachelor’s degree in AI, data science, or engineering related area (Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline)
- 10+ years of work experience in data science, or engineering, or operations for a diverse range of projects
- 2-3 years of hands-on experience leading data science or AI/ML deployment and operations teams
- Track record of managing stakeholder groups and effecting change
- Recognized by peers as an expert in production deployment and AI/ML ops with deep expertise in CI/CD and DevOps for monitoring and orchestration of data science workflows, and hands-on development
- Understands how to synthesize facts and information from varied data sources, both new and pre-existing, into clear insights and perspectives that can be understood by business stakeholders
- Clearly articulates expectations, capabilities, and action plans; actively listens with others’ frame of reference in mind; appropriately shares information with team; favorably influences people without direct authority
- Clearly articulates scope and deliverables of projects; breaks complex initiatives into detailed component parts and sequences actions appropriately; develops action plans and monitors progress independently; designs success criteria and uses them to track outcomes; engages with stakeholders throughout to ensure buy-in
- Manages projects with and through others; shares responsibility and credit; develops self and others through teamwork; comfortable providing guidance and sharing expertise with others to help them develop their skills and perform at their best; helps others take appropriate risks; communicates frequently with team members earning respect and trust of the team
- Experience in translating business priorities and vision into product/platform thinking, set clear directives to a group of team members with diverse skillsets, while providing functional & technical guidance and SME support
- Ability to manage projects from end-to-end, from requirements gathering through implementation, hypercare, and development of support processes to ensure longevity of solutions
- Demonstrated experience interfacing with internal and external teams to develop innovative data science solutions
- Strong understanding of data science development lifecycle (CRISP)
- Deep experience with CI/CD integration (e.g. GitHub, GitHub Actions or Jenkins)
- Deep understanding of MLOps principles and tech stack (e.g. MLFlow)
- Experience working in a cloud based analytics ecosystem (AWS, Snowflake, etc)
- Highly self-motivated to deliver both independently and with strong team collaboration
- Ability to creatively take on new challenges and work outside comfort zone
- Strong English communication skills (written & verbal)
PREFERRED QUALIFICATIONS
- Advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems or related discipline
- Experience in solution architecture & design
- Experience in software/product engineering
- Strong hands-on skills for data and machine learning pipeline orchestration via Dataiku (DSS 10+) platform
- Hands on experience working in Agile teams, processes, and practices
- Pharma & Life Science commercial functional knowledge
- Pharma & Life Science commercial data literacy
- Experience with Dataiku Data Science Studio