Insights & Analytics Senior Specialist

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 asAWSorMicrosoft Azure and use technologies such asDocker 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

• Bachelors degree inComputer Science Data Science Artificial Intelligence Engineering Mathematics Statistics or a related discipline or equivalent professional experience.

• Typicallyat least five years of experienceas 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 innatural language processingorcomputer vision. Understanding of modern deep learning architectures including the role ofattention mechanismsand transformer-based models.

• Strong programming experience inPython 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 preferablyAWS or Microsoft Azure to develop train deploy or operate machine learning solutions.

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