Sr Business System Analyst

Redwood City, CA, United States

This autonomous role will enable the project through:

BA (Business Analyst) Planning: Work with Informatics teams and scientific groups to plot detailed Business Analysis activities and refine project plan; inclusive of setting expectations and timelines

Stakeholder Mgmt. & Communications: Coordinate stakeholder assessment and management of stakeholders/sponsors. Facilitate effective communication and collaboration between business stakeholders, technical teams, and other project stakeholders. Act as a liaison to ensure shared understanding, manage expectations, and resolve any conflicts or ambiguities related to requirements.

Process Mapping: Understand the workflows, processes, and datasets involved in the various business processes and their evolution in a data-centric environment. The Analyst will document data pipelines from the business processes needed to enable computational scientists & data scientists.

Requirements Gathering: Collaborate with IT Business Partners, Product Owners, business users, and technical leads to understand the detailed needs, objectives, requirements, and data elements needed for each data product. Conduct interviews and analysis to elicit and document clear and comprehensive functional and non-functional requirements. This includes clear mapping of business rules, security, and data governance needs of each data product Owner & Producer.

Data Analysis and Modeling: Collaborate with information modelers and domain experts to understand data requirements, data sources, and data processing needs. Facilitate the creation of data models, data mapping, and data transformation specifications to ensure data integrity, quality, and availability. Translate business needs into comprehensive System to Target Mappings (STTM) for each data product - connecting the business insight & modeling needs to the specific data elements in and across systems and data sources. Furthermore, identify necessary process & system remediations to ensure successful data capture for robust data products.

Solution Design: Collaborate with digital capability managers, architects, and data engineers to ensure successful development of effective, usable, and trustworthy data products.

User Acceptance Testing (UAT): Develop UAT plans, test cases, and scenarios based on requirements. Coordinate with business users & predictive modelers to conduct UAT, facilitate defect tracking, and ensure that the developed data product meets user expectations.

Documentation and Knowledge Management: Maintain accurate and up-to-date project documentation, requirements, design documents, STTM materials, test cases, and user guides. Enable knowledge asset retention and necessary activities to ensure longer-term data product continuity.

Maintain Agile & Growth Mindset: Support agile approaches and participate in lessons learned sessions and provide feedback to improve future data product development processes. Stay updated with industry trends, emerging technologies, and best practices related to data product development and analysis.

Skills & Experience Required:

1. Strong large molecule domain knowledge and familiarity with the drug discovery process, including understanding of early-stage drug discovery assays, data types, and scientific principles. Knowledge of relevant bioinformatics and predictive biological modeling is beneficial; Knowledge/experience in learning and/or applying AI/Client to drug discovery.

2. Proven ability in gathering, tracing, translating, and managing complex requirements, business rules, and data from varied stakeholders. Inclusive of mapping business processes, user stories, and functional and non-functional requirements. Strong attention to detail to ensure accuracy and completeness of requirements, documentation, and deliverables necessary.

3. Strong technical aptitude necessary. Ability to navigate and extract information and insights from IT systems and databases, and familiarity with information modeling, data integration methodologies, and data management principles is valuable. Knowledge of programming languages, data analysis tools, or data visualization platforms is a plus.

4. Exceptional proficiency in analyzing data, deriving insights, and presenting findings. Skills in data modeling, statistical analysis, data visualization, or machine learning techniques are advantageous.

5. Ability to think critically and analytically to understand complex business and technical requirements. The analyst should be adept at breaking down problems, identifying patterns, and deriving insights from data.

6. Excellent oral and written communication skills including technical writing/documentation; organizes and presents ideas in a convincing way.

7. Exceptional interpersonal and outgoing personality skills; able to collaborate effectively with cross-functional teams, including data scientists, lab researchers, developers, and business stakeholders. Effective communication skills are crucial to establish rapport, manage expectations, and facilitate collaboration.

8. Agile and Growth Mindset; must possess a willingness to learn innovative technologies, methodologies, and scientific concepts related to early drug discovery. Ability to adapt to evolving project needs, embrace new challenges, and stay updated with industry trends and best practices.

9. Possess strong business acumen; possess a broad, enterprise-wide view and understanding of strategy, processes, and capabilities, enabling technologies, and governance

10. Formal Business Analysis Certification (IIBA (International Institute of Business Analysis) ECBA/CCBA/CBAP) or Data Science / Data Analysis Certification, a strong plus