Machine Learning, Ai

**Machine Learning, AI - Research Scientist for Casuality**

**Barcelona (ESP), **Gothenburg (SWE), Cambridge (UK),** Gaithersburg (**US), Mississauga - **Toronto (**CAN)**

**Competitive Salary + Benefits**

**Make a more meaningful impact to patients’ lives around the globe**

Here you’ll have the opportunity to make a meaningful difference to patients’ lives. With science at its heart, this is the place where breakthroughs born in the lab become transformative medicines - for the world’s most complex diseases. Answer unmet medical needs by pioneering the next wave of science, focusing on outcomes and shaping the patient ecosystem. With our ground-breaking pipeline, the outlook is forward-thinking. Be proud to be part of a place that has achieved so much, yet is still moving forward. There’s no better time to join our global, growing enterprise as we lead the way for healthcare and society

**What you’ll do**

In this role, you will design, implement, test, and analyze machine learning algorithms to help contribute to the overall improvement and automation of the drug discovery pipeline. You will work on feasibility studies, discovery, development, and deployment of machine learning models. You will be required to interact extensively with other teams across the organization, and our academic partners with the goal of delivering products in a timely manner. It is expected that you will present to various partners, represent us at conferences, and publish your findings in scientific journals or top conferences such as ICML, ICLR and NeurIPS.

Accountabilities
- Work efficiently in a team of 3-10 (led by a Senior AI Researcher) to optimally deliver projects using the latest ML methods and modern engineering standard processes
- Analyze challenges from the drug development process and provide creative solutions from the fields of representation learning, reinforcement learning, active learning, statistical learning theory, causality, ranking and recommendation
- Remain at the forefront of ML Research by participating in journal clubs, mentoring, contributing publications and personal development projects.

Examples of projects this team works on are de-novo design of biologics, generative synthetic data models of our clinical trial data, and multi-modal domain translation.

**Essential for the role**
- PhD degree in computer science, electrical engineering, statistics, applied mathematics, related field, or a completed MSc and at least 3 years of experience in developing machine learning models.
- At least 3 years of experience in developing machine learning models.
- ML Ops experience: model tracking, model governance, multiple models in different production contexts.

**Desirable for the role**
- A research work, **especially in the context of causal inference**, demonstrated by journal and conference publications in prestigious venues (with at least 1 publication as a leading author). Examples include but are not limited to: NeurIPS, ICML, UAI, COLT, AAAI, ICLR, JMLR, etc.
- A high impact model deployment (e.g., developed over a period of 2+ years) or 2+ small-to-medium scale deployments can be considered as an equivalent experience.
- Deep understanding of drug development and clinical trial process and data (all phases).
- Track record of collaborating successfully with AI engineering teams to deliver complex machine learning models.

**Why AstraZeneca?**

At AstraZeneca we’re dedicated to being an excellent Place to Work. Where you are empowered to push the boundaries of science and unleash your ambitious spirit. There’s no better place to make a difference to medicine, patients and society. An inclusive culture that champions diversity and collaboration, and always committed to lifelong learning, growth and development. We’re on an exciting journey to pioneer the future of healthcare.

**Why we love it**

**So, what’s next?**
- Are you already imagining yourself joining our team? Good, because we can’t wait to hear from you.
- 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.

**Where can I find out more?**

**#CAI**

**#DataAI**

**#DSAI