Senior Manager, Data Science Development Operations
1 month ago
Working with Us
Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams rich in diversity. Take your career farther than you thought possible.
BMS Hyderabad is an integrated global hub where our work is focused on helping patients prevail over serious diseases by building sustainable and innovative solutions. This important science, technology, and innovation center will support a range of technology and drug development activities that will help us usher in the next wave of innovation.
Summary: We are seeking an experienced individual to join an exceptional team that creates advanced analytical solutions to support Development Operation at Bristol Myers Squibb. This position will provide hands-on data science and machine learning engineering support and leadership for an end-to-end ecosystem of solutions for in-line / pipeline, short- / long-term. Specifically, this role will manage a team of Data Scientists who will code, validate and test machine learning and/or simulation models, and develop analytics apps to allow users to interact with these models.
Roles & responsibilities
Manage a sub-team of 15-20 Data Scientists to develop robust and scalable machine learning (ML) or simulation models. Supervise deliverables, ensuring timely completion, high quality, and stakeholder acceptance. Model factors like epidemiology, patient flow, competition, and others impacting drug development/commercialization. Drive the execution and interpretation of AI/ML models from framing problems to shaping solutions. Extract insights from epidemiology, real world data, market research and other sources to support patient simulations. Stay informed about advancements in pharma forecasting methodologies and tools. Engage key stakeholders to understand their needs and translate them into technical specifications. Align closely with the US-based team and project leaders on priorities, project planning and management. Work closely with data scientists and domain experts to ensure compatibility of data science & AI solutions with other tools. Work with IT as needed on data management and infrastructure related to advanced analytics solutions. Support the deployment of AI/ML solutions, striving for seamless integration with existing systems. Develop UI apps to allow stakeholders to effectively interact with new forecasting solutions. Collaborate with IT to optimize data pipelines and establish efficient data management practices. Support opportunities to enhance the business impact of AI/ML models, such as quality and efficiency. Document solution specifications (e.g., architecture, data flows, algorithms) to ensure transparency / reproducibility.Skills and competencies
Strong analytical thinking and problem-solving skills with the ability to analyze complex data sets and draw meaningful conclusions. Proficiency in statistical analysis machine learning, and data modeling Advanced NLP techniques and deep learning methodologies Strong communication skills with the ability to present complex information to non-technical stakeholders in a clear manner. Strong business acumen and strategic thinking, with the ability to translate analytical findings into actionable insights and recommendations.Experience
Bachelor’s or master’s degree in computer science, Engineering, Applied Math, Bio Informatics, Operations Research or equivalent working experience in a highly analytical field 5-7 years’ experience working as a Data Scientist or similar role, preferably focusing on pharma revenue forecasting. Effective communication and collaboration skills Demonstrated ability to manage multiple projects simultaneously and deliver high-quality results within specified deadlines. Preferably, experience advanced analytics techniques such as imputation, ML engineering, hyper parameter tuning, MLOPs as well as off-the-shelf solutions. Experience in data processing and visualization tools, and statistical software packages (e.g., SQL, R, Python, Tableau) Deep interest in the drug development process, and preferably, experience with pharmaceutical industry analytics including data sources, market research, R&D data, or real-world data.
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