Data Scientist (Generative AI)

ABOUT NORSTELLA

Norstella is a group of prominent pharmaceutical solutions providers – Evaluate, MMIT, Panalgo, The Dedham Group, Citeline – that help clients navigate complexities at each step of the drug development life cycle, from pipeline to patient. For more information, please visit Norstella.com.

Evaluate is a global company providing outstanding market intelligence services for the Pharmaceutical, Medical Device, Financial and Consulting sectors, through the Evaluate Pharma®, Evaluate Medtech®, Evaluate Omnium and Evaluate Vantage® online brands. Our international clients in Pharma and Biotech, Medtech, Banking and Consultancy regard Evaluate Pharma® 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.

THE TEAM

In this role as a Data Scientist you will report into the Lead Data Scientist for Strategic Intelligence and Market Access, within the Data Science Department. You will design and deploy cutting-edge models to support the creation of new products. We have small multi-functional teams consisting of pharmaceutical industry experts, R&D, data engineering and data scientists to rapidly prototype new visualisations and interactive reports using both our existing and newly acquired datasets.

SCOPE OF THE ROLE

In this role as a Data Scientist you will:

• Apply your fundamental knowledge of machine learning, generative AI, and other DS techniques to identify opportunities and help shape product development
• Use Python and AWS SageMaker to create knowledge generation algorithms applied to pharmaceutical products
• Visualise and analyse the performance of your models with common python libraries
• Work with data engineers and DevOps to deploy your models into production environments
• Contribute to our growing in-house data science library of useful functions, data handlers, APIs and other modules

HOW YOU’LL SUCCEED

Ultimately our goal is to smooth patient access to life-saving therapies. You will work with R&D pharma specialists to understand a problem which is hindering developing and releasing effective new pharma products which we believe we can help with. After understanding the problem you will conceptualise potential solutions; in the past our solutions have involved classical machine learning, fine-tuning large language models, or simply well-designed data transformations and business logic – we’re about elegant solutions to problems rather than the technology used.

After conceiving potential solution(s), you will work with data engineering to collate data from a wide variety of sources and develop your algorithm as a proof-of-concept. You will deliver indicative results from your PoC into datasets for visualisation and exploration by the broader multi-functional team. You will also perform code reviews with the data science team to explain your approach and its strengths and weaknesses.

After iterating the design with the multi-functional team as part of customer-led product development, you might convert your prototype into a full product. This will involve productionising your code to a high standard, containerisation, and deployment of your algorithm, usually as an API in AWS SageMaker. Over time you may revisit this product, re-evaluate its performance, and retrain/improve as required.

Essential Requirements

• 4+ years of experience with python and core data science libraries including pandas, numpy, sklearn, scipy, CatBoost, XGBoost and other similar libraries
• Degree at Masters level or better in a STEM field such as Maths, Physics, Computer Science, Engineering, or equivalent practical experience
• Ability to design and iterate creative solutions in Jupyter notebooks
• Ability to convert successful code into well-engineered packages with appropriate use of in-built classes, modules, high/mid/low level functions, and other python best practice
• Excellent statistical knowledge especially in relation to training dataset weaknesses and DS model scoring
• Ability to work with stakeholders to manage your projects independently
• Ability to explain your technical decisions on a project to non-data scientists

Nice to have

• Experience with AWS especially in relation to ML workflows with SageMaker, serverless compute and storage such as S3 and Snowflake
• Exposure to building products that leverage large language models and generative AI
• Knowledge of the pharmaceutical industry, in particular the stages of pharmaceutical product development
• Data visualisation skills with Matplotlib