Location: Gaithersburg, USA

Hybrid: 3 days a week onsite

Insights & Analytics Senior Specialist translates complex business and scientific challenges into practical, scalable machine learning solutions 

The role contributes across the machine learning lifecycle, from understanding the problem and assessing the available data through to model development, deployment, monitoring, and continuous improvement. It requires the ability to make sound technical decisions, explain them clearly, and balance innovation with the expectations of a regulated and quality-focused environment. 

Typical Accountabilities 

• Translate needs into solutions: Work with stakeholders to understand business or scientific problems, assess whether machine learning is an appropriate approach, and define clear objectives, success measures, and delivery plans. 

• Develop machine learning solutions: Design, build, evaluate, and improve models using appropriate statistical and machine learning techniques. This may include deep learning approaches for structured, text, image, or other unstructured data. 

• Work with advanced AI methods: Apply modern approaches such as natural language processing, generative AI and AI Agents/Workflows based on the problem being addressed. Select methods based on their suitability, performance, maintainability, and governance requirements. 

• Deliver production-ready capabilities: Work beyond experimentation to ensure that models can be packaged, deployed, integrated with relevant systems, monitored, and maintained over time. Contribute to the design of reliable and scalable AI architectures. 

• Use cloud and engineering practices: Develop and deploy solutions using cloud platforms such as AWS or Microsoft Azure, and use technologies such as Docker, source control, automated testing, and continuous integration and delivery to support consistent and reproducible delivery. 

• Maintain quality and governance: Define appropriate data and model quality criteria, validate results, document assumptions, and consider issues such as explainability, bias, privacy, security, performance degradation, and responsible use of AI. 

• Communicate with clarity: Present technical findings and recommendations in a way that is meaningful to both technical and non-technical audiences. Communicate model performance, uncertainty, limitations, and risks openly so stakeholders can make informed decisions. 

• Provide technical leadership: Contribute to technical design discussions, code and model reviews, reusable components, engineering standards, and communities of practice. Provide guidance and informal mentoring to colleagues where appropriate. 

• Operate as an individual contributor: Deliver work within agreed scope and priorities, influencing through technical expertise, collaboration, and sound judgement rather than through formal line management. 

• Work within the relevant country remit: Comply with applicable local policies, standards, regulatory expectations, and organizational requirements. 

Qualifications and Skills 

Essential 

• Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, Statistics, or a related discipline, or equivalent professional experience. 

• Typically at least five years of experience as a Data Scientist, Machine Learning Engineer, AI Engineer, or in a related role. Experience in healthcare, pharmaceuticals, life sciences, or another regulated and data-intensive environment is particularly relevant. 

• Demonstrated experience taking machine learning work from problem definition and data preparation through to model evaluation and, deployment or operational use. 

• Experience working with text, images, or other unstructured data, including relevant methods in natural language processing or computer vision. Understanding of modern deep learning architectures, including the role of attention mechanisms and transformer-based models. 

• Strong programming experience in Python, with the ability to write maintainable, tested, and reusable code. Experience working with databases, APIs, and data pipelines is also expected. 

• Experience using at least one major cloud platform, preferably AWS or Microsoft Azure, to develop, train, deploy, or operate machine learning solutions. 

• Experience with Docker and familiarity with software engineering and MLOps practices such as version control, testing, deployment automation, experiment tracking, monitoring, and model lifecycle management.