Machine Learning Engineer

Introduction to project

We are an Agile team that uses knowledge graphs and machine learning in collaboration with our scientists to help develop better drugs faster, choose the right treatment for patients, and run safer clinical trials. The team is a wide mix which includes Software Engineers, Machine Learning Engineers, Data Scientists, Ontologists, and Bioinformaticians. We are looking for a Machine Learning Engineer to be embedded within our team and become a core pillar of our work.

Accountabilities

We are seeking a knowledgeable and passionate Machine Learning Engineer to join our team. At the core of our work lies systems biology. It drives the design of our data products, our analytical pipeline, and our machine learning approaches. In this role, you will play a crucial part in weaving together experimental data coming from multiple experimental modalities and our in-house knowledge graph to make the best out of modern machine learning developments for drug discovery. As a Machine Learning Engineer, you will have the freedom to unleash your creativity and leave a lasting impact by shaping our engineering and scientific strategies. Our supportive and encouraging environment will provide the necessary resources to thrive and excel in your work.

What you'll do:
- Collaborate closely with chemists, biologists, and other scientists to design and implement computational strategies for analyzing complex biological datasets, including genomics, proteomics, and other omics data.
- Develop and apply machine learning algorithms and models to extract meaningful insights from large-scale biological datasets, identify patterns, and predict outcomes.
- Implement data pre-processing, feature extraction, and dimensionality reduction techniques to enhance the quality of input data for machine learning models.
- Contribute to the design and optimization of experiments, ensuring the collection of high-quality data for analysis.
- Create and maintain pipelines for data processing, analysis, and visualization, ensuring reproducibility and scalability.
- Stay up to date with the latest advancements in computational biology, machine learning, and related fields, and integrate innovative methods into our research projects.
- Collaborate with cross-functional teams to present findings, share methodologies, and contribute to project discussions.
- Contribute to scientific publications, conference presentations, and grant proposals.

Essential Skills/Experience
- Ph.D. or master's degree in computational biology, bioinformatics, computer science, or a related field.
- Strong foundation in computational biology, genomics, and molecular biology concepts with focus on biological databases/ontologies such as Uniprot, Ensembl, Gene Ontology, and similar.
- Proven expertise in machine learning techniques including, but not limited to, deep learning, tree-based methods, unsupervised learning, and feature selection.
- Proficiency in programming languages (preferably Python) and experience with relevant libraries and frameworks (e.g., pandas, tensorflow, pytorch, scikit-learn, networkx).
- Experience with processing and analyzing large-scale biological datasets, e.g., Next-Generation Sequencing data (including single-cell datasets), proteomics data.
- Familiarity with data visualization tools and techniques to effectively communicate results to both technical and non-technical stakeholders.
- Excellent problem-solving skills and the ability to think critically about experimental design and data interpretation.
- Strong communication skills and the ability to work collaboratively in a multidisciplinary team.

Desirable Skills/Experience
- Publication record in relevant scientific journals or conference proceedings is a plus.
- Experience working with graphs, graph analytics, and network science in general.
- Experience with software development: CI/CD, linting, type-checking, good programming practices, and working knowledge of git.

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. Join us in our unique and ambitious world.

At AstraZeneca, we combine cutting-edge science with leading digital technology platforms and data to transform our ability to develop life-changing medicines. Our dynamic environment offers countless opportunities to learn and grow while making a meaningful impact on patients' lives. With investment behind us and a commitment to innovation, there's no better place to make a big impact.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

Ready to take on this exciting challenge? Apply now!

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.