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Gaithersburg, Maryland, United States
Hybrid
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.
The annual base pay for this position ranges from $93,868.00 - $140,802.00 USD. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.
Are you ready to be part of a talented, cross-functional team working together to improve lives and make the biggest possible impact for patients, science and society?
Why AstraZeneca?
When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility.
Apply now!
Date Posted
17-Aug-2026Closing Date
30-Aug-2026Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.