As a highly collaborative individual, you enjoy solving problems that focus on adding business value, and you have a sense of ownership, enjoying hands-on technical work.
You will collaborate with business stakeholders, engineers, and internal teams to build and implement extraordinary pharma-focused data products (re-usable assets) and solutions, delivering them right to the client with the utmost importance. You will also be responsible for developing deep Life Sciences domain understanding in at least one of the following areas: Manufacturing, Procurement, Supply Chain, Chemical Discovery, Molecular / Materials Optimization, Clinical Trial Design and Operationalization, Real World Evidence, and Commercial.
You will build real-world scalable machine learning pipelines and deploy them to production, operate at the intersection of data science and software engineering to create analytics solutions, and produce high-quality code that allows us to put solutions into production. You will lead the thinking on choosing and using the right analytical libraries, programming languages, and frameworks. You will also build analytics libraries and tools based on project experience and the latest research, refactor code into reusable libraries, APIs, and tools, and play an active role in leading team meetings and workshops to inform product development and process evolution.
In this role, you will learn how successful projections on real-world problems across Life Sciences use cases are completed through referencing past deliveries of end-to-end pipelines. You will build products alongside the Core Engineering team and evolve the engineering process to scale with data, handling complex problems and advanced client situations. You will be focused on the wrangling, clean-up, and transformation of data by working alongside the Data Science team, which focuses on modeling the data. You will use new technologies and problem-solving skills in a multicultural and creative environment.
You will work on the frameworks and libraries that our teams of Data Scientists and Data Engineers use to progress from data to impact. You will guide global companies through data science solutions to transform their businesses and enhance performance across industries including healthcare, automotive, energy, and elite sport. You will also experience real-world impact through unique international learning and development opportunities. You will benefit from work that fuses technology and leadership, using the latest technologies and methodologies within first-class learning programs at all levels. You will collaborate within multidisciplinary teams of data scientists, engineers, project managers, UX and visual designers who work together to enhance performance. You will be part of an innovative work culture where creativity, insight, and passion come from balance, supported by wellness initiatives, insightful talks, and training sessions. You will also experience a diverse environment, with colleagues from over forty nationalities, recognizing the benefits of working with people from all walks of life.
You will work in multi-disciplinary global Life Science–focused environments, harnessing data to provide real-world impact for organizations globally. Our Life Sciences practice focuses on helping clients bring life-saving medicines and medical treatments to patients. This practice is one of the fastest-growing practices and is comprised of a tight-knit community of consultants, research, solution, data, and practice operations colleagues across the firm. It is also one of the most globally connected sector practices, offering ample global exposure. The Life Sciences.AI (LS.AI) team is the practice’s assetization arm, focused on creating reusable digital and analytics assets to support our clients’ work. LS.AI builds and operates tools that support senior executives in pharma and device manufacturers, for whom evidence-based decision-making and competitive intelligence are paramount. The team works directly with clients across Research & Development (R&D), Operations, Real World Evidence (RWE), Clinical Trials, and Commercial to build and scale digital and analytical approaches to addressing their most persistent priorities.