Insights & Analytics Senior Specialist

AstraZeneca; Gaithersburg (MD); Hybrid; USD 94,000 - 141,000; Full time

Job summary

AstraZeneca in Gaithersburg, MD is seeking an Insights & Analytics Senior Specialist to translate complex business and scientific challenges into practical, scalable ML solutions. You will contribute across the ML lifecycle from problem understanding to deployment and monitoring.

The role requires clear communication, technical leadership, and adherence to regulated, quality-focused environments. Hybrid work with three onsite days per week.

Qualifications

• Bachelor's degree in CS / data science / AI / eng / math / stats or equivalent.
• Typically five years of experience as a data scientist, ML engineer, or AI engineer in regulated environments.
• Experience taking ML work from problem definition to deployment or operational use.
• Experience with text, images or unstructured data including NLP or CV; understanding transformers.
• Strong Python programming with data pipelines, databases, and APIs.

Responsibilities

• Translate needs into ML objectives, success metrics, and delivery plans with stakeholders.
• Design, build, evaluate, and improve models for structured/unstructured data.
• Apply NLP, CV, and transformer-based approaches where appropriate.
• Deliver production-ready capabilities deployed, monitored, and integrated with systems.
• Define data/model quality criteria; address explainability, bias, privacy, and governance.

Skills

Python programming

NLP

Computer vision

AWS / Azure

ML Ops

Data pipelines

Education

Bachelor's degree or equivalent

Tools

Docker

Git / VCS

APIs

SQL

Job description

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.

Essential Qualifications and Skills

• Bachelor’s 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.

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.

Date Posted

17-Aug-2026

Closing Date

30-Aug-2026

Our 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.