Data Science Engineer II

Key Skills

Java Programming, Python Programming, SQL, Agile methodologies, Big Data Technology, Machine learning techniques, R Programming

Job Description

You would be supporting new GSK’s purpose of uniting science, talent, and technology to get ahead of disease together. Our purpose is to develop tools, models, and processes to enable scientists to create preventative products such as vaccines or discover ways to treat illnesses such as therapeutics. We also support the equally important development of robust and efficient manufacturing of such products. Examples of specific areas of interest include delivering end-to-end biomarker data pipelines; delivering integrated data analytics environments to aid scientists in target identification or models that drive more efficient cell line development. We are a team with an ambition to be at the cutting edge of the pharmaceutical industry data science and engineering. So, as an R&D department with a passion for using data to solve problems and improve patient care, why wouldn’t you consider working for us

Our organization is built on the principles of ownership, accountability, continuous development, and collaboration. Our leaders are committed to your career and development from day one. You will be working in a stimulating and supportive environment as part of a dynamic, growing community. You will have access to our Learning & Development resources, comprising a blend of on-the-job learning with targeted coaching, peer-to-peer mentoring, expert-led workshops, and a network of learning providers.

The Team?

The Data Science and Visualisation team (DSV) is an experienced team of data scientists and software engineers that focuses on the creation and deployment of leading-edge, reusable data science microservices and products. This might be the creation of genetic and genomic data science products, changing the decision cycle time for our biopharmaceuticals, or accelerating assets in clinical trials through smart site selection and forecasting. The ease of use and interpretation in the hands of the scientist is very important to us, hence the emphasis upon not just the data science but the visualization.

The Role?

The Data Science Engineer II is a hands-on position where you will be empowered to be creative, ambitious, and bold. Given a defined business or scientific objective, you will apply machine learning to the relevant data to iterate over model choices metrics to produce high-quality predictive models. The data science engineer will accomplish this by writing fantastic code along with the proper unit, functional, and integration tests for code to ensure quality.

Key Deliverables

As a Data Science Engineer II, you will:

• Evaluate and explore the data for the intended task and will be able to design and create models that perform to requirements and specifications.
• Ship ML models into production and understand how a model’s requirements or the available data might change over time and design an ML product that will be able to cope with these requirements.
• Evaluate other options if the machine learning problem fails, the engineer should be able to try other architecture features, featurization techniques, cost functions, etc., in a way that will reliably improve the performance metrics for that model.
• Compare results from various methodologies and recommend the best techniques to stakeholders and be able to defend and explain the rationale to stakeholders.
• Connect and collaborate with subject matter experts in biology, chemistry, and medicine and offer, challenge, and solicit advice via peer review to enhance quality, share best practices and embed innovation.

Why you?

Basic Qualifications:

We are looking for professionals with these required skills to achieve our goals:

• BS Degree (Mathematics or Engineering, Data Science/ML, Statistics, Computer Science, Physics, Computational Biology, Computational Chemistry,) with 4+ years of experience (or MS with 2+ years of experience, or PhD) in the biotech/pharmaceutical/healthcare/diagnostics/health insurance space
• Experience designing, developing, and implementing analytical solutions using a variety of commercial and open-source tools (deep experience using common tools like Python and/or R would be required)
• Experience comparing results from various methodologies then explaining the technical concepts at all levels in the organization, including senior managers/stakeholders
• Experience creating algorithms to extract information from large, multiparametric data sets and deploy them into production to realize significant value

Preferred Qualifications:

If you have the following characteristics, it would be a plus:

• Experience in the development of interpretable machine learning to production using a programming language such as Python or R
• Experience in manipulating data in a big data environment – experience with hive, SQL, and the like would be helpful
• Experience in the biotech/pharmaceutical/healthcare/diagnostics/health insurance space