Role Title: Content Data Science Lead
Location: London/ Hybrid
Package: up to £130k base + benefits
Our client is a group of prominent pharmaceutical solutions providers that help clients navigate complexities at each step of the drug development life cycle, from pipeline to patient.
They are a global company providing outstanding market intelligence services for the Pharmaceutical, Medical Device, Financial and Consulting sectors, through multiple online brands. Their international clients in Pharma and Biotech, Medtech, Banking and Consultancy regard their solution as the industry’s gold standard for timely and accurate analysis of reported drug sales, consensus sales forecasts, R&D pipeline, markets and comprehensive company financials.
This role is very flexible hybrid, you can choose to work from wherever you want. Either the home or the office in central London.
They use data science to enrich, analyse and provide detailed insights on the pharmaceutical landscape. They do not just sell raw data; they deliver answers you can act on.
The Data Science Department consists of several teams: Strategic Intelligence, Market Access, Clinical, and Consulting, all of which are mature and well-functioning teams. However, the core of the business is the acquisition of data. This area is a significant investment for the business, and they have ambitious plans over the next three years to develop a new content ingestion platform to unify their content teams across five different business units. They are building this to be AI-first, where the human team role will be to correct and train the ML models to improve performance over time. As such, they are looking for a leader to manage a new DS team within the department focused on this core part of the business.
SCOPE OF THE ROLE
They are looking for an experienced and capable leader to build and develop this team:
- Work with the Head of Data Science to ensure the practices and methodologies are aligned with the group;
- Manage the team of NLP data scientists already in the business;
- Develop and build out the roadmap for achievable and productive projects during your first few months as you speak to others in the business;
- Expand the team to include technical business analysts, ML engineers and related roles within the Content team to support the roadmap;
- Inherit and improve the existing DS frameworks they have developed for NER (drugs, companies, diseases, routes of administration, and other terms);
- Work with the engineering team to make these algorithms available in UI interfaces for data ops;
- Work with ML Engineers to make active learning and continual improvement a core part of this process.
HOW YOU’LL SUCCEED
The company's goal is to smooth patient access to life-saving therapies. One can’t do this without high quality data to provide to customers in the pharmaceutical sector, and to drive their product-facing data science algorithms.
They are currently rebuilding their process to collect this data, using a blend of sources both public and proprietary. This is a green-field build, so you will not be constrained by legacy processes or code.
Their sources include biomedical research documents, company trading updates and annual reports, web crawlers, clinical trial documents, and pharmaceutical claims data from the US covering hundreds of millions of lives.
You will work with the team initially to apply the existing techniques and frameworks they have to this ingestion process, while learning about any gaps in our capabilities. As a next step, you will hire additional people to fill those gaps and start working on the roadmap you will have laid out.
You will build a high performing team, manage projects to successful completion as well as mentor data scientists to develop their skills and ensure their approach to the projects is robust and reliable. You will be expected to be hands-on, review their code and methodology as well as help them present their work to the broader data science team. Each project will require metrics to describe performance, and have a plan for improvement in the future, once more ground-truth data is returned from the human curators.
WHAT IT TAKES
- A degree in a relevant field, such as computer science, mathematics, data science or a related field;
- Extensive data science experience including NLP experience;
- Exceptional problem-solving skills and the ability to think strategically;
- Understanding of pharmaceutical lifecycle;
- Proven experience of building and managing high-performance data science teams;
- Expert level Python skills;
- Passion for and knowledge of the cutting edge in data science, as well as proven traditional NLP methods;
- Experience of deploying ML models in the cloud, particularly AWS;
- Being detail-oriented and highly organised – an effective planner;
- Great communication skills, in particular for finding opportunities and shaping new research and DS services for the business.
- Commercial mindset;
- Ability to learn and adjust quickly;
- Ability to shape the team and role as the business continues to develop;
- Passion for communicating the art of the possible within Data Science, particularly language models.
For more information, please contact Guy Bevington at: