Level

AstraZeneca

Associate Director, Forward Deployed Decision Science

AstraZeneca is hiring an Associate Director, Forward Deployed Decision Science for a remote role open to applicants in United States. Level rates it ; you can apply on AstraZeneca.

Level's read on this job

  • On Level's scale, this forward deployed engineer role is Works on AI ●●●○: the daily work is on AI products, without building the model.
  • The posting lists this role as remote.
  • Tools and skills the posting cites include Databricks, AWS and Snowflake.
  • Level lists 1,200+ other open roles at AstraZeneca.
  • The role was posted in the last few days.

AI in this role

Drive commercial decision-making and business outcomes by designing AI-enabled solutions, machine learning models, and agentic workflows.

databricksawssnowflakeazure
ai-agentsmachine-learningdata-sciencedecision-scienceanalytics

Associate Director, Forward Deployed Decision Science


About the Role

Do you want to be the person the business calls when a hard commercial question needs a real answer? In this forward-deployed role, you will sit side by side with Alexion’s commercial leaders to understand their challenges first-hand, shape them into analytical and AI-enabled solutions, and bring the answers back in clear, practical language that drives decisions and action.


This is a business-facing Decision Science role - You will own the connection between the business problem and the analytics that solve it.


What You'll Do


• Partner directly with the business: Embed with indication teams, Brand Marketing, Sales Operations, Insights, sales leadership, RDUs, and RAMs to understand their needs and priorities.

• Translate needs into solutions: Convert business questions into the right mix of data science, machine learning, analytical models, AI-enabled solutions, or decision frameworks.

• Deliver measurable outcomes: Develop and apply analytical and machine learning approaches to commercial problems such as sales effectiveness, targeting, patient finding, promotional investment, and other relevant business use cases, and own the connection to business outcomes.

• Make the complex simple: Turn sophisticated analytical output into clear, jargon-free recommendations that stakeholders can act on with confidence.

• Lead with agentic AI: Design and orchestrate AI agents and human-agent workflows that accelerate analysis and decision-making, applying current AI-native approaches alongside established analytical methods.

• Build on the cloud: Use cloud platforms such as Databricks, AWS, Snowflake, Azure, or comparable environments as the foundation for scalable and efficient analytical solutions.

• Bring fresh thinking: Operate independently, challenge the status quo, and bring new ideas and expertise that can strengthen the team’s capabilities.

• Safeguard trust: Apply sound statistical judgment and validate AI-generated outputs while working within data privacy requirements, de-identified data practices, and applicable AZ AI governance standards.

What You'll Bring

Essential

• Education: Master’s degree or PhD in Data Science, Statistics, Biostatistics, Engineering, Economics, Applied Mathematics, or a related quantitative field.

• Relevant experience: Typically 4+ years of experience applying decision science, data science, advanced analytics, or quantitative methods to real business problems. Strong and recent experience with modern AI and analytics is more important than total years alone.

• Demonstrated business impact: Track record of applying analytics in a commercial environment such as sales effectiveness, commercial operations, brand marketing, or a relevant adjacent area such as supply chain.

• Cloud computing: Hands-on experience with Databricks, AWS, Snowflake, Azure, or a comparable cloud/data platform. Exact platform experience can vary.

• AI-native and agentic workflows: Practical, recent experience using AI-enabled approaches, coding agents, or agentic workflows, including human-agent interaction and orchestration.

• Technical foundation: Working proficiency in Python and SQL, with practical experience in machine learning, predictive modeling, statistical analysis, and model validation. Depth may vary by model family and use case; mastery of every method or library is not required.

• Stakeholder management: Strong communication and consulting skills, with demonstrated ability to engage non-technical leaders, simplify complex topics, and influence decisions without relying on technical jargon.

• Independent delivery: Ability to make an impact quickly, operate with autonomy, and bring new expertise rather than requiring significant training to become effective in the role.


Desirable


• Healthcare or life sciences: Experience in pharmaceutical, biotechnology, healthcare, life sciences, rare disease, or a healthcare practice within consulting or technology.

• Commercial data: Familiarity with commercial healthcare data such as claims, CRM, specialty pharmacy, patient services, or comparable datasets.

• Commercial analytics use cases: Experience with areas such as marketing mix modeling, next-best action, segmentation, forecasting, patient identification, adherence, or field effectiveness.

• Visualization: Experience with Power BI, Tableau, Qlik, or comparable tools for communicating analytical findings and enabling business decisions.


Why This Role Matters

Your work will help teams use data, advanced analytics, and AI more effectively to identify opportunities, improve commercial decision-making, and focus resources where they can have the greatest impact for patients living with rare diseases.


In our dynamic environment, be supported by the brightest minds working together side by side. Agile and collaborative, we work cross-functionally, as well as externally, to thrive at pace. Be appreciated for your contributions. Our culture of recognition and reward means each of us feels supported and empowered to step up and take responsibility. With all the tools you need to excel at every level, coupled with ongoing constructive coaching, there’s no better place to bring out your best.


Are you ready to bring new ideas and fresh thinking to the table? Brilliant! We have one seat available and we hope it’s yours.

 

Date Posted

22-Oct-2026

Closing Date

04-Nov-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.

How we rate this

Associate Director, Forward Deployed Decision Science at AstraZeneca rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsMachine learningData ScienceDecision ScienceAnalyticsDatabricksAWSSnowflake

Also common in forward deployed engineer jobs at this level

Pay

Typical forward deployed engineer job
$182k

This posting doesn't list pay. Use the typical forward deployed engineer figure when pay comes up. See forward deployed engineer jobs

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. Tell me about a project where machine learning was part of your work. What did you do?
  3. Tell me about a project where data science was part of your work. What did you do?
  4. Tell me about a project where decision science was part of your work. What did you do?
  5. Tell me about a project where analytics was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI agents, Machine learning, Data Science, Decision Science, and Analytics. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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