Drug safety scientist job description

Receives, assesses, investigates, processes, and reports adverse drug events regarding company drug products in accordance with SOPs and FDA regulations and guidelines. Participates in global data exchange with Fresenius Kabi entities and other Fresenius Kabi USA partners. Interacts with the Quality and Regulatory Departments to resolve safety and quality issues.
Responsibilities

Reviews, assesses and processes adverse drug events.Reviews and assesses the medical literature for adverse drug event case reports. Assist the global Adis Coordinator as required, ensuring alert settings and literature search profile in the Adis database is up to date.Participates in the preparation of adverse event Periodic Adverse Drug Experience Reports (PADER), Periodic Safety Update Reports (PSUR), and expedited case reports.Maintains current knowledge of applicable local and global regulations, standard operating procedures, and guidelines. Maintains an in-depth understanding of product knowledge, labeling, and relevant data for company products. Maintains knowledge of all pertinent regulatory safety publications.Interacts with the Quality and Regulatory Departments to resolve safety and quality issues.

RequirementsMedical/clinical professional degree required i.e., PharmD, RPh, RN degree. Minimum 3 years of drug safety and adverse drug event reporting experience required. Pharmaceutical industry experience preferred.Experience with preparation of adverse event Periodic Adverse Drug Experience Reports (PADER), or Periodic Safety Update Reports (PSUR) is preferred.Clinical/hospital experience preferred.Ability to understand and interpret federal regulations and company operating procedures as they apply to medically complex adverse drug events and determine whether the events suggest a product quality issue.Ability to gather data from multiple sources and references and formulate a medically-appropriate case narrative.Ability to interpret large amounts of safety and quality-related data and recognize/identify potential health hazards (signal detection).Must be able to prioritize and multi-task with minimal supervision,and participate in peer-review process when processing product complaints and adverse drug events.Knowledge of PC systems and Microsoft Office Suite (Word, Excel) required. Lotus Notes experience preferred.

Medical Benefits Effective First Day of Employment

We offer an excellent salary and benefits package including medical, dental and vision coverage, as well as life insurance, disability, 401K with company match, and wellness program.

Fresenius Kabi is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, citizenship, immigration status, disabilities, or protected veteran status.

Additional Information

We offer an excellent salary and benefits package including medical, dental and vision coverage, as well as life insurance, disability, 401K with company match, and wellness program.

Fresenius Kabi is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, citizenship, immigration status, disabilities, or protected veteran status.

About Us
Syngenta's Seeds Product Safety group is committed to applying world-class science and winning innovation to offer farmers safe, cost-effective solutions that address the demands of a rapidly changing world. We are looking for a scientist with experience in protein biochemistry and/or analytical science to join our Protein Characterization and Analysis Team. This role will be based at our World Class Research Triangle Park facility in Durham, North Carolina.
As a scientist (Technical Expert 4) in the Team, you will design and execute protein characterization studies and perform analytical method validations under Good Laboratory Practices (GLP) in support of safety assessments conducted for regulatory submissions for our genetically modified crop products.

Accountabilities - You will:

Independently plan, design, execute, monitor and deliver protein characterization studies employing insect in support of safety assessments of our genetically modified crops in compliance with all safety, regulatory, and GLP requirements

Play a lead role in identifying, solving, and managing a variety of technical issues or challenges, providing technical guidance to team members in technical area of expertise

Characterize microbial and plant proteins from different and complex matrices using standard biochemical methods like protein quantitation, SDS-PAGE, Western blot, glycosylation analysis, enzyme activity assessments

Develop and validate quantitative protein methods (i.e., enzymatic activity methods, ELISA) for application in plant protein expression and protein characterization studies

Document experiments and test results, interpret data, and write and review complex technical documents (protocols, reports, SOPs)

Ensure technical procedures are in alignment with regulatory expectations and meet international standards

Act as project lead or key participant in designated projects.

Serve as GLP-Study Director or Study Manager accountable for timely delivery of studies conducted as part of comprehensive regulatory data packages in support of the global registration of genetically modified food crops.

Lead projects and/or represent Seeds Product Safety in multi-disciplinary regional or global project development teams, providing key input in the development of techno-regulatory and safety strategies

Remain abreast of current research to identify potential opportunities and issues.

Flagship Pioneering conceives, resources, and builds companies across both human health and sustainability. Flagship has created over 100 scientific ventures resulting in >$200 billion in aggregate value, 500+ issued patents, and >50 clinical trials, spanning Moderna Therapeutics, Generate Biomedicines, Indigo Ag, Tessera Therapeutics, and others. We harness science and entrepreneurialism to envision alternative futures, beginning with seemingly unreasonable propositions and navigating to transformational outcomes through an iterative, evolutionary methodology. We call this process “pioneering”.

We are looking for extraordinary computational scientists, engineers, and entrepreneurs to work alongside individuals within the Flagship Ecosystem focused on solving the most impactful challenges in AI across both human health and sustainability. We collaborate, encourage failure, trust one another, and celebrate successful solutions to hard problems. We respect the diversity of opinion - because we value the freedom to explore hunches.

Position Summary

We believe deep integration of data-driven machine learning with experimental approaches will be a core driver of the next generation of defining companies in health. We aim to upend the traditional approach to molecular discovery towards one characterized by intentionality, programmability, and speed by developing methods for molecular design and generation that can reliably generalize across functions and applications. Modalities with potential across these applications span scientific areas across biology, chemistry, physics, and beyond; we believe that immense impact potential in human can come from diverse scientific and machine learning backgrounds - and are open to all profiles with computational excellence.

To this end, we are seeking creative, motivated Machine Learning Scientists to develop and apply our core technologies for ML-enabled molecular generation and ML-enabled Bayesian optimization, active learning, and experiment design in biology. You will join companies and explorations at the early stages of our company creation process to develop innovative methods for molecular generation and modeling, leveraging both in-house and external data to train and evaluate models while also deploying new algorithms into production and integrating deeply into experimental platforms. The successful candidate will work closely with experimental scientists to rapidly advance various scientific programs.

Key responsibilities:

• Develop novel machine learning models and algorithms for data-driven molecular design and hone them through deployment on experimental platforms.
• Advance and evaluate the state of the art for machine learning models connecting molecular forms, features, structures, and function, spanning applications such as design of sequence-based molecules, structure prediction, complex prediction, and function learning.
• Develop, advance, and evaluate the state of the art for machine learning methods for developing surrogate models and acquisition functions, spanning Gaussian processes and functions like maximum probability of improvement and expected improvement, as well as approaches based on deep learning, variational models, as well as other areas of continual innovation in the field.
• Use our integrated data platform to devise models able to leverage measured labels “in-the-loop”.
• Work with experimental groups to tailor modeling efforts toward high-impact applications.
• Develop production-quality code in a team setting and plan for deploying and training models at scale.
• Present progress from scientific work in regular research meetings and prepare reports and slide decks for broader internal and external communication.

Qualifications:

• PhD in a computer science, statistics, or a related field with demonstrated experience applying computational methods to scientific applications
• 3+ years of experience with developing Machine Learning methods to solve scientific problems, with a particular interest towards applications to molecular generation, active learning, Bayesian optimization and/or experimental design as well as adjacent fields such as biology, chemistry, immunology, or genomics
• Experience developing, debugging, and applying models using modern deep learning frameworks
• Foundational knowledge on Bayesian optimization and experimental planning methods including methods for uncertainty quantification and probabilistic modeling such as Gaussian processes, variational methods, MCMC techniques, and conformal prediction.
• Proficiency in Python and machine learning frameworks such as Tensorflow, Pytorch, and/or JAX
• Energetic self-starter with the ability to work effectively in an entrepreneurial environment
• Excellent analytical skills and ability to synthesize & communicate complex information rapidly and effectively
• Unmatched sense of urgency
• A deep passion for using novel machine learning techniques to unlock new impact potential across health
• No ego

Nice to have:

• Foundational knowledge around probabilistic machine learning and optimization methods
• Practical experience developing deep generative models (e.g., autoregressive models, VAEs, Flows, GANs, EBMs, Transformers, etc.)
• Publications in major ML conferences or scientific journals that apply ML to problems in the sciences, including but not limited to molecular biology, chemistry, physics, structural biology, genetics, or other key questions that center around molecular prediction, design, and/or generation
• Demonstrated experience developing software in a team setting
• Experience with optimizing performant code

Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Recruitment & Staffing Agencies : Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.