Machine Learning Technical Specialist

Company: Bristol Myers Squibb
Location: Princeton, New Jersey, United States
Type: Full-time
Posted: 31.AUG.2021
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Summary

At Bristol Myers Squibb, we are inspired by a single vision - transforming patients' lives through science. In oncology, hematology, immunol...

Description

At Bristol Myers Squibb, we are inspired by a single vision - transforming patients' lives through science. In oncology, hematology, immunology and cardiovascular disease - and one of the most diverse and promising pipelines in the industry - each of our passionate colleagues contribute to innovations that drive meaningful change. We bring a human touch to every treatment we pioneer. Join us and make a difference.

Position Summary

Machine Learning (ML) Technical Specialist will be responsible for project deliverables related to strategic initiatives such as Data on Demand, Digital Twins, DataOps and MLOps. As part of the Data Management & Platforms team, they will serve as technical specialist for ML-driven solutions for scientific R&ED. Additionally, they will help guide the team towards the optimal ML strategies for the problems we are solving.
The role responsibilities require high level of digital innovation, and demand passion, speed, accountability and creative problem solving. The candidate will actively work towards solving business needs identified in the scope below, and will be a contributing resource in the development of ML capabilities for R&ED. The candidate will collaborate with other functional teams and digital innovation teams to ensure the optimal portfolio of applications exists to support the business. THIS OPPORTUNITY IS IDEAL FOR A RECENT MS GRADUATE OR CURRENT PHD STUDENT WHO WOULD LIKE TO USE THIS 1-YEAR ASSIGNMENT AS A CASE STUDY FOR THEIR THESIS. FOLLOWING THE 1-YEAR PERIOD THERE MAY BE AN OPPORTUNITY FOR AN EXTENSION OF THE ASSIGNMENT OR A FULL-TIME ROLE WITHIN BMS.

Key Responsibilities

- Identify opportunities to apply the latest advancements in ML to solve problems across scientific domains (genomics, proteomics, metabolomics etc)
- Demonstrate a combination of business focus, strong analytical and problem-solving skills, and programming knowledge to improve operational efficiency (i.e. laboratory instrument data processing)
- Apply ML-tools/algorithms to extract information from large, multiparametric data sets
- Ensure fit-for-purpose solution approach:
- Create analytical/modeling solutions while maintaining the right balance between speed and analytical soundness
- Ensure the right level of solution complexity is selected to solve business demands quickly and efficiently
- Design, develop and implement analytical solutions using commercial and open-source tools
- Compare results from various methodologies and recommend best techniques to stakeholders, while making impactful contributions on emerging machine learning methodologies
- Identify external resource (vendors, partners, bio-IT startups) that could provide products and services at scale
- Develop and embed automated processes for predictive model validation, deployment, and implementation and educate the team on these approaches
- Influence team-level ML strategy for projects by actively contributing with ideas and test solutions:
- explore design options to assess efficiency and impact
- develop approaches to improve robustness and rigor
- contribute to the planning and direction of a project and effectively prioritize goals
- Present external work and research, applicable to biopharmaceutical R&D
- Apply agile methodology and distributed decision-making using evidence and applying judgement to balance pace, rigor and risk

 
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