Analytics Engineer II

Requirements

• We welcome a bachelor's, master's or Ph.D. degree in a relevant field such as Data Science, Statistics, Computer Science or a related discipline.
• Proven experience (typically 2 to 5 years) in a data science role, preferably within the biopharma or pharmaceutical industry
• Strong background in statistical analysis, machine learning, and predictive modelling techniques
• Experience in handling and analyzing large-scale structured and unstructured data sets using SQL, NoSQL or similar technologies
• Demonstrated ability to develop and implement predictive models and machine learning algorithms
• Experience working with healthcare data, clinical trials, or related domains is highly desirable
• Solid understanding of the biopharma industry, including regulatory requirements and healthcare data sources
• If you come across a role that intrigues you but doesn't perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.
• Uniquely Interesting Work, Life-changing Careers

Benefits

Additional Information

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. Take your career farther than you thought possible. Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us . Roles & Responsibilities Hands-on design, deployment, operations, maintenance of pipelines for data science related products and machine learning solutions, ensuring reliability, scalability, and maintainability. Develop and maintain cutting-edge tools and frameworks empowering data scientists to work efficiently and effectively. Develop a close working relationship with data science teams to facilitate and implement best practices relating to machine learning engineering, data engineering, and MLOps. Collaborate with stakeholders to define project objectives, formulate data-driven hypotheses and identify KPIs for analysis Gather, pre-process and explore large-scale structured and unstructured data from diverse sources, including clinical trials, patient records and genetic data Conduct exploratory data analysis (EDA) to identify patterns and anomalies in the data and propose solutions to business problems Develop and implement predictive models (e.g., regression, clustering, time series forecasting) to solve complex business challenges Collaborate with data engineers and IT teams to ensure data availability, quality, and reliability for analysis and modeling Communicate complex analytical findings and insights to both technical and non-technical stakeholders through clear and compelling visualizations, reports and presentations Stay up-to-date with the latest methodologies and best practices in statistical analysis, machine learning, and the biopharma industry Mentor and provide guidance to junior data scientists and actively participate in knowledge sharing and team development Skills and competencies Experience with building and deploying data science and data engineering solutions using established industry methods (MLOps, Git) to pharmaceutical related datasets is preferred Proficiency in programming languages such as Python or R for data manipulation, analysis and modeling Familiarity with data visualization tools such as Tableau, Power BI or matplotlib/seaborn for effective communication of findings Strong understanding of experimental design, hypothesis testing and A/B testing methodologies Excellent problem-solving skills and the ability to think critically and creatively to tackle complex business challenges Excellent communication and presentation skills to convey complex concepts to technical and non-technical stakeholders Experience with cloud platforms (e.g., AWS, Azure) and big data technologies (e.g., Hadoop, Spark) is a plus Strong organizational and time management skills, with the ability to prioritize tasks and meet deadlines in a fast-paced environment

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