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Our client is a leading research-based biopharmaceutical company. They apply science and their global resources to deliver innovative therapies that extend and significantly improve lives. Every day, colleagues work across developed and emerging markets to advance wellness, prevention, treatments and cures that challenge the most feared diseases of our time.
ROLE SUMMARY
Do you want to make an impact on patient health around the world? Do you thrive in a fast-paced environment that brings together scientific, clinical, and commercial domains through engineering, data science, and analytics? Then join Pfizer Digital’s Artificial Intelligence, Data, and Advanced Analytics organization (AIDA) where you can leverage cutting-edge technology to inform critical business decisions and improve customer experiences for our patients and physicians. Our collection of engineering, data science, and analytics professionals are at the forefront of Pfizer’s transformation into a digitally driven organization that leverages data science and advanced analytics to change patients’ lives. The Data Science Industrialization team within Data Science Solutions and Initiatives leads the scaling of data and insights capabilities – critical drivers and enablers of Pfizer’s digital transformation.
As the Data Science Software and Solutions Development Lead, you will be a leader within the Data Science Industrialization team charged with building key business applications powered by data science workflows with embedded advanced analytics/AI/ML. As the champion driving productized data science software thinking to enable industrialized data science, you will lead a global team and partner with cross-functional business stakeholders and platform & data leaders to catalyze identification, design, iterative development, and continuous improvements of data science code accelerators, reusable data science components, software, and solutions. Your team will develop best practices and maintain standards for software engineering including design and maintenance of code repositories and plugin libraries to standardize deployments, enable self-service and re-use, and accelerate data science and analytics. In addition, you will be responsible for providing critical input into the analytics ecosystem & platform strategy to promote self-service, drive productization & collaboration, and foster innovation. Your team will be accountable to key Pfizer business functions (including Pfizer Biopharma, R&D, PGS, and Finance) for building AI/ML powered data science software and solutions that support major business objectives across all of Pfizer’s core business units.
ROLE RESPONSIBILITIES
- Lead design, development, and delivery of data science applications, code accelerator repositories, plugin accelerator libraries, and best practices in the CI/CD development of software and solutions with embedded data science capabilities
- Lead development of and provide oversight for managed service lines related to data science software and application development and development of enterprise-wide code accelerators and production-level plugin and widget accelerators
- Advance CI/CD workflow and code management capabilities, implement best practices, and contribute to the broader talent building framework by facilitating related trainings
- Provide day-to-day leadership, supervision, and mentorship for a global team with technical & functional expertise that includes data science, AI/ML engineering, and software engineering
- Coach direct reports through technical thought leadership and innovation
- 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 AIDA Platforms team on end to end capability integration between enterprise platforms and internally developed data science embedded software, solutions, code repositories, and plugin libraries
- Partner with AIDA Platforms team to define best practices for self-serve production deployment of data science software and applications to reduce effort, turn-around times, and simplify maintenance and support
BASIC QUALIFICATIONS
- Bachelor’s degree in data science software engineering related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline)
- 7+ years of work experience in software engineering for a diverse range of projects
- 2-3 years of hands-on experience leading data science or software engineering teams
- 2-3 years of work experience in data science or AI/ML engineering
- Track record of managing large stakeholder groups and effecting change
- Recognized by peers as an expert in software engineering with deep expertise in CI/CD for data science software development, and hands-on development and deployment experience within data science enabling technologies such as Dataiku Data Science Studio, Domino Data Science Platform, Cloudera Data Science Workbench, AWS Sagemaker, Databricks, or other data science platforms
- 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
- Demonstrated experience interfacing with internal and external teams to develop innovative data science solutions
- Strong understanding of data science development lifecycle (CRISP)
- Strong hands-on skills in analytics engineering, data science, and software development (e.g., Python, R, SQL, industrialized ETL software)
- Experience working in a cloud based analytics ecosystem (AWS, Snowflake, etc)
- Experience working with various types of data (structured / unstructured)
- Experience in data ingestion, data warehousing, and data model concepts
- Experience with relational and dimensional data structures, theories, and practices
- Experience in CI/CD integration (e.g. Git Hub, Git Hub Actions or Jenkins)
- Hands on experience working in Agile teams, processes, and practices
- 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)
