Naveen Singla, Data Science Center of Excellence lead at Bayer
The Research & Development process for life sciences companies is expensive, time-consuming, requires collaboration among many data scientists, and involves multiple, complex models.
The nature of life science work is experimental and collaborative; models must constantly be tracked, retrained, and iterated on to reflect changing data and other factors that lead to model drift. Unlike software engineering or data management, models require a research-based approach comprised of constant exploration, iteration, and agility.
Collaboration among large and distributed data science teams, along with citizen data scientists, is challenging when they have diverse skill sets and preferences for tools and technologies.
Open and flexible ease of use: Flexibility, agility, and scalability to support a dynamic tooling environment and diverse skill sets and preferences. Data scientists can focus on driving innovation, using their preferred hardware, software, tools, and languages. Team members who are still learning can process, explore, and model data using the latest packages. Data scientists at every level can control their own environment and hardware.
Build institutional knowledge with all research available for review, saved discussions, and true collaboration.
Collaboration: Domino automatically versions code and entire experiments along with data, environments, discussion threads, and necessary artifacts. Work is never lost and is always reproducible. Data scientists across the globe can collaborate and build on past work instead of reinventing the wheel.
Adoption: As teams grows, expert data scientists can create templates in Domino that help ingrain and share best practices for more junior colleagues.