| Company: | AstraZeneca |
|---|---|
| Job Role: | Data Scientist for Digital Health, Artificial intelligence, Data Visualization, Oncology R&D Strategy |
| Experience: | 3-8 years |
| Vacancy: | 100+ |
| Qualification: | BE/BTech, ME/MTech,MS Ph.D |
| Salary: | 1,85,000$ |
| Location: | United States, New York, Cambridge, England, United Kingdom, Waltham, Massachusetts, Gaithersburg, Maryland |
| Join us on Telegram | Click Here |
| Apply Mode: | (Online) |
| Deadline: | Not Mentioned |
In AstraZeneca, we collaborate on the development and delivery of innovative medicines to patients internationally. Our team makes an impact and finds solutions to challenges. Our commitment to doing what is right, no matter how difficult the situation may be, is what drives us forward. By utilizing data science, the Digital Health Oncology Data Science Team aims to help patients and clinical trial staff experience a more personalized patient experience. Digital solutions will be deployed to clinical trials and in real life to reduce the burden on patients. In order to tackle the pressing problems in Oncology, the team will exploit data from clinical trials, Real World Evidence (RWE), clinical free text, medical imaging, and Patient Reported Outcomes (PROs).
Our team is looking for a Principal Data Scientist who can develop innovative machine learning methods to address patient burden using diverse datasets, such as clinical trial data, renal function data, imaging data, genomic data, and behavioral data. Among the dozens of applications of machine learning are forecasting, data analysis, analytical graph-building, time series, Natural Language Processing (NLP), and reinforcement learning. Partnering with the team and developing innovative digital approaches to reduce clinical trial burdens and patient burden in real-world settings will be a significant role of this position. In addition to working closely with the DH Oncology Team, the Data Scientist will develop novel approaches to modeling and inference from multimodal data. Application candidates should be well-versed in statistics and should have experience applied to a production environment. We are working on a variety of projects, including machine learning models for developing digital biomarkers. We are also developing patient risk stratification algorithms, solving problems of survival analysis, linking medical imaging with omics data, and identifying or validating new drug targets.
Minimum Requirements:
- You must hold a Master's degree in quantitative science (such as mathematics, computer science, or engineering) or have demonstrated substantial experience in the appropriate data science methodologies.
- Experience in the biomedical, financial, tech, or finance sectors of 3+ years
- Skills in programming, data science and version control (bitbucket/git), UNIX/Fermi knowledge, and experience with cloud computing (AWS preferred) are essential.
- SysOps expertise in cloud computing, Kubernetes, sophisticated knowledge of machine learning, infrastructure as code, and experience with machine learning products.
- Expert experience in machine learning operations: tracking models, governance, multiple models used in different production scenarios
- Working experience with time series analysis and forecasting, behavioral analysis, and early machine learning applications
- A broad knowledge of mathematical and statistical modeling techniques and the motivation to continue learning and developing them.
- Ability to communicate effectively, coordinate business analysis, and consult with partners, as well as identify solutions via dynamic decision making
Desirable
- The preferred degree is a Ph.D or BE/BTech, ME/MTec h Expert in any Specialization
- Pharmaceutical industry experience
- Models of advanced machine learning including transformer-based NLP, reinforcement learning, GNNs, and time series and forecast models of the highest level
- Interactive metrics (interactive dashboards using DASH & static visualizations) & data visualization & interactive metrics
Why AstraZeneca?
AstraZeneca exploits any opportunity to make change happen, because regardless of how small an opportunity may seem, it can contribute to the future of the company. Being entrepreneurial involves finding and recognizing those moments of potential that can lead to life-changing medicines. Come experience what it's like to build a new kind of biopharmaceutical company to redefine what's possible. This means we're pioneering new methods, bringing together unexpected teams, and opening new ways of working. What do you think? Let's go on a journey together.
Taking the next step is as simple as applying now!
In order to be considered for this exciting opportunity, you will need to fill out the online application at your earliest convenience - this is our only way to know that you are well qualified for the position. You might know someone who is a good fit for this position, please let them know
Diversity and equality are core values at AstraZeneca. In addition to building an inclusive and diverse team of all backgrounds, we aim to harness industry-leading skills and leverage as many perspectives as possible. Inclusion is key to our success, so we are striving to be as inclusive as possible. Regardless of their background, all qualified candidates are welcomed and considered for our team. In addition to following all applicable laws and regulations on non-discrimination in employment (and recruitment), we also verify employment eligibility and work authorization.
Vacancy: 100+
Number of Role: 4
1) Responsibilities for the Role Principal Data Scientist - Digital Health
- Advises AstraZeneca on data science solutions and provides advanced data science expertise.
- AstraZeneca projects benefit from sophisticated data science solutions, appropriately presented by non-technical partners.
- Providing a variety of services that support the achievement of project objectives within established frameworks.
- In addition, he or she is constantly learning from senior colleagues, proposing appropriate courses for personal development.
- Review of working practices to ensure noncompliant processes are raised
- Responsible for ensuring compliance with the Clinical Development process.
- Experience collaborative relationships with global leaders in data science, biology, statistics and IT.
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2) Responsibilities for Data Scientist (Data Visualization)
- Analyzes data to recommend data science solutions for AstraZeneca projects.
- Proposes non-technical partners with advanced data science solutions to AstraZeneca.
- Manages a variety of tasks that contribute to the success of projects within established frameworks.
- In addition, he or she is constantly learning from senior colleagues, proposing appropriate courses for personal development.
- Conducts reviews of working practices and escalates non-compliant activities
- Ensures compliance with Clinical Development standards.
- Experience collaborative relationships with global leaders in data science, biology, statistics and IT.
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3) Responsibilities for Intelligence Data Science Director, Oncology R&D Strategy
Role: Oncology Research & Development
Oncology Research & Development has a broad pipeline of next-generation medicines aimed primarily at the treatment of breast, ovarian, lung and hematological cancers, as well as other types of tumors. Personalized healthcare and biomarker technologies are being used to target these cancers on four key fronts - immunotherapy, genetic drivers of cancer and resistance, DNA damage repair, and antibody drug conjugates. Research data needs to be brought together in a holistic and synergistic manner in order for these focus areas, new modalities and interventions to be successful.
Together, we are deeply committed to empowering patients living with cancer – aiming to eliminate cancer as a cause of death in the future. There's no better place to make a difference - here we are uniquely positioned in terms of the funding, the ambition, and the courage to make a difference.
Oncology R&D Strategy Group and the company's Competitive Intelligence and Analysis team are at the center of AstraZeneca's industry-leading R&D strategy. As an oncology R&D strategist, you will be accountable for ensuring the decisions we make concerning our pipeline assets are made in the most effective manner possible, with the goal of maximizing return on investment and achieving the best patient outcomes.
Data science is relevant to shaping R&D strategy through deep competitor insights, and we have an exciting opportunity for someone who is passionate about utilizing data science in this way. Providing high-level analysis and insight to senior leaders and key stakeholders is critical to guiding strategy and influencing investments in the rapidly evolving oncology landscape.
The individual will work within an industry-leading competitor analysis team to generate insights through AI and ML, build scalable analytics apps and integrate external and internal insights to ensure robust investment decisions across the AZ portfolio.
What you'll do:
- Identify and lead data science projects of defined scope within the Oncology R&D strategy
- Influence functional practices and strategy by adopting ongoing knowledge and awareness of trends, standard methodologies, and new developments in analytics and data science
- Models and other computational tools that help guide complex decisions during the clinical development process will be proposed, developed, and implemented
- Coordinate with other data science and IT teams to architect, develop, maintain, and document computational infrastructure for diverse data sources, including clinical trials, real-life data, literature, conference proceedings, analyst reports, and so forth.
- Create tools and workflows for extracting relevant details from scientific literature and other unstructured sources via natural language processing
- Integrate data from external sources (e.g. CT.gov, the Cancer Genome Atlas, FDA/EMA Structured Product Labels, and the Cancer Imaging Archive) and connect them to in-house pipelines
- Use data visualization techniques for effective presentation of information to internal and external stakeholders
- Ensure reliable, effective, and efficient use of information and technology within the project portfolio, including creating and nurturing effective relationships with senior and executive stakeholders.
- Transform complex problems into appropriate data problems, models, and analytical solutions for healthcare, pharma, and oncology through your healthcare, pharma, and oncology-specific expertise
- Bring jointly R&D IT, enterprise IT, and the Data Science Office together Critically think through data challenges, and develop data solutions by showing learning agility to quickly understand the issues
- Critically think through data challenges, and develop data solutions by showing learning agility to quickly understand the issues
Essential for the role:
- An advanced degree in bioinformatics, data science, biomedical informatics, computer science, analytics, or another quantitative field is required
- Expertise in at least one programming language suited for high-throughput data analysis (Python and R are preferred).
- I have significant experience with applying machine learning (both traditional and deep learning) to a variety of areas, including Natural Language Processing and predictive modeling
- Data structures, data modeling, and ontologies for relational and non-relational databases
- Data science and computational biology skills, including knowledge graphs and regression/classification tools based on network-based methods
- Extensive experience in healthcare/life science (oncology is preferred)
- Knowledge of information engineering and information architecture, with extensive experience designing and delivering data projects.
- Experience managing data, integrating data and connecting data across life science R&D
- Experience and knowledge of Competitive Intelligence data and solutions
The role requires the following:
- (Preferable) University PhD in computer science, a biomedical field of informatics, or a related field of quantitative science
- Tools & methods used in imaging informatics experience / knowledge
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4) Responsibilities for Diagnostic Scientist - Digital & Artificial Intelligence
Follow the science and Pioneer new frontiers:
Our team is dedicated to fighting cancer with a goal of eliminating the disease as a cause of death. We are united by a common vision.
As we continue to develop our innovative pipeline, we continue to seek out multiple indications and high-quality molecules. Breakthroughs are achieved by combining ground-breaking science with the latest technology. Six new molecular entities are anticipated to be delivered by 2025, thanks to investment.
Our culture fosters collaboration, courage and curiosity so we can make informed decisions based on clinical data. Taking smart risks and asking questions in order to write the next chapter for our pipeline and oncology team.
We have built a science community that is both outstanding internally and internationally by pioneering collaborative research. Bringing together some of the greatest medical centers in the world, we have united academia and industry.
Join a team that is committed to improving the lives of millions of cancer patients and build an exciting and meaningful career.
The Advanced Biosamples and Precision Medicine business unit at AstraZeneca aligns medicine development with patient outcomes by delivering diagnostic assays that allow personalised healthcare and complement drug development.
Do you have experience implementing digital solutions in clinical settings using your Machine Learning (ML)/Artificial Intelligence (AI) expertise?
Precision Medicine and Biosamples, within the Tissue Diagnostics team, is seeking candidates to lead or support initiatives to demonstrate the usefulness of ML/AI in clinical trials and in the real world to help doctors identify patients who will benefit most from AstraZeneca drugs.
Besides working with multidisciplinary teams, you would also be responsible for overseeing
In terms of
- identifying and evaluating technologies relying on machine learning and artificial intelligence that can solve difficult precision medicine challenges;
- bringing the scientific aspects of machine learning and artificial intelligence into clinical trials;
- Provide scientific evidence for the development and commercial launch of diagnostic tests, as well as their regulatory submissions.
Typical Accountabilities
Applying scientific, technical, and operational expertise to manage projects in a global environment. Develop drug and diagnostic development projects by identifying opportunities, proposing solutions, and working across scientific boundaries.
Our digital solutions include Machine Learning and Artificial Intelligence to promote tissue-based diagnostics. The role may involve working with AI companies and clinical operations scientists to create and implement methods for clinical testing laboratories, as well as with regulatory, commercial, and medical affairs teams to deliver applications to patients and clinicians.
Our drug and diagnostic development programs will be guided by our data analytics projects, which are performed end-to-end to generate scientific insights.
Organize conferences and publish peer-reviewed publications to communicate scientific results to colleagues within and outside of the lab.
- A commitment to deliver quality work on time and within budget.
- Informing the appropriate governance bodies about the progress, risks, and opportunities of the agreed deliverables for review, challenge, and issue resolution.
- A small supervisory role or roles that involve skills transfer and training.
Essential
- Experience or a master's degree related to the field of study
- Knowledge of how to work in a collaborative environment
- Communicate scientific concepts clearly to non-experts with excellent verbal and written
Skills Required:
- Machine learning and/or statistics knowledge
- Expertise in Python and R programming
- Numpy, pandas, PyTorch, TensorFlow/Keras, ScikitLearn, Seaborn, tidyr, caret, ggplot, shiny (cited libraries are examples; specific knowledge of these libraries is not required).
- An understanding of reproducible data science tools, such as notebooks (such as Jupyter, R Markdowns) and version control (such as git).
- Desirable
- Having a PhD in a related field or equivalent experience
- Expertise in solving complex medical questions with machine learning
- A working knowledge of digital pathology and/or the analysis of biomedical images
- Knowledge of cloud computing technologies such as Amazon Web Services and Microsoft Azure
- It is an advantage to have experience with tissue-based diagnostic tests (e.g. immunohistochemistry or in situ hybridization), but it is not a requirement
Why we love it....
When it comes to science and being part of a team that makes a difference in patients' lives, there is no better place to do it. Our pipelines are strengthened and developed here since we use science daily.
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