The Data Scientist role will report to the Head of Digital Innovation & Data Sciences and wield a unique ability to engage business executives, design experiments, spearhead analytical projects, and build automated tools to positively impact the workplace. This role is responsible for collaboratively developing and implementing a cohesive multi-year analytics strategy and roadmap to improve the workforce experience, automate business processes, enable data-driven decisions, and gain cost efficiencies across all Site Services functions. This role has the decision making authority for all analytics solutions across site services. This role will be part of the Site Services Technology Leadership Council and wisely guide the digital innovation and investment for Genentech's SSF location as it strives to be a more connected campus/mini smart city.
Key responsibilities of this position include:
- Develop and execute multi-year Site Services analytics strategy in partnership with Site Services Leadership Team. Ask the right scientific questions, understand the evidence needs for Site Services, ideate and make recommendations on fit-for-purpose data and analytics solutions.
- Develop strategic plans to access fit-for-purpose data sources to support predictive and prescriptive analytics, and gain access to data through collaboration or data generation.
- Develop a comprehensive and deep understanding of the data we work with and foster learning with colleagues using analytical tools and applications to broaden data accessibility and advance our proficiency/efficiency in understanding and using the data appropriately.
- Contribute ideas, design surveys, and build algorithms that will influence people and business outcomes across Genentech and Roche. Be an active thought partner and strategic consultant to our Site Services leaders and other partners from design to execution across the experiments we run, the surveys we design, the analyses we execute, and the dashboards/apps we build.
- Stay current with and adopt emergent and leading analytical methodologies, tools and applications to ensure fit-for-purpose and impactful approaches.
- Accountable for end to end analysis: apply rigor in study design and analytical methods; plan for data processing; design a fit-for-purpose analysis plan, assess effective ways of presenting and delivering the results to maximize impact and interpretability so that it resonates with the respective audience; implement and/or oversee the study, including its reporting; ensure compliance with applicable pharma industry regulations and standards.
- Communicate findings to Executive bodies such as: Site Advisory Committee, GNE officers, Site Services Leadership Team, and other business stakeholders; publish results internally and with external industry publications as appropriate; participate in external conferences, meetings and forums to present your insights and leading practices (e.g. congress/conference).
- Develop understanding of Genentech/Roche data environment and build relationships/collaborate/contribute with key local, regional and global stakeholders (partners, customers, and broader analytics community). Collaborate and contribute to functional, cross-functional, enterprise-wide or external data science communities, networks, collaborative groups, initiatives on knowledge-sharing, methodologies, innovation, technology, IT infrastructure, policy-shaping, processes, etc. to enable broader and more effective use of data and analytics.
- Communicate complex statistical concepts to non-technical audience in both written and verbal mediums; provide coaching and strategic consulting regarding use and interpretation of data.
- Influence senior business leaders through strong communication skills and deep subject matter expertise in order to turn insights into action and impact
- MSc, PhD or similar qualification in a quantitative data science discipline (e.g., statistics/biostatistics, epidemiology, bioinformatics, health economics, computational biology, computer science, mathematics, outcomes research, public health, biology, medicine, psychology)
- Minimum of 13 years relevant business experience, post academia--delivering models, algorithms, and automated tools that drive business decisions and improve business outcomes--and providing leadership and strategic consulting in Data Science. Experience in pharmaceutical industry is preferred
- Proven ability to translate and communicate complex study design and findings to diverse audiences. Excellent communication skills (presenting, influencing and storytelling), including earning rapid credibility and resonating at the executive level
- Deep subject matter expertise in data science with proven ability to transfer this expertise across the business; proven track record of setting new standards, advancing the field of expertise (internally and externally) and engaging & influencing executive leaders internally and externally.
- Experience in survey design to develop reliable/valid measures and sampling representative data
- Proficiency with classical statistics and machine learning approaches to fit models and estimate their performance; Experience automating and productionalizing machine learning models / algorithms using R and/or python
- Experience working with software engineers to build web apps and microservices
- Experience creating data visualizations and dashboards that distill complex analyses into meaningful and accessible insights and recommendations
- Demonstrated track record of developing and executing data science research projects and data analyses (e.g., real world data, surveys) with publications and presentations
- Experience developing shared code (e.g., git and unit testing, programming patterns, libraries/packages), creating data pipelines, and putting models into production
- Proficiency in one or both of R and python
- Fluency in SQL and RDBMS
- Demonstrated experience with managing project scope and driving delivery in an evolving environment requiring proactive leadership and effective problem-solving and prioritization when faced with challenges
- Demonstrated strong collaboration skills internally and externally with partners and peers
- Experience managing and leading cross discipline teams, particularly leading by influence vs. authorityDemonstrated entrepreneurial mindset and self-direction, ability to teach others and willingness to learn new techniques
- Comfortable employing agile methodology
- Intellectual curiosity and ability to handle high levels of ambiguity