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Junior Faculty Investigator - Clinical Informatics

Faculty/ Pharmacoepidemiology and Pharmacoeconomics, DOM  

GENERAL SUMMARY/ OVERVIEW STATEMENT:

We are a world-leading research division at Harvard Medical School that studies the use, effectiveness and safety of medical products in clinical practice. We have direct access to large amounts of up-to-date patient-level longitudinal data from electronic health records and insurance claims. In addition to the ongoing applied research activities, we have a portfolio of methods-focused projects funded by the FDA and NIH, many of which aim to improve estimation of causal treatment effects by making increasing use of unstructured and semi-structured data from electronic medical records.

The Junior Faculty position will be the centerpiece of all clinical informatics activities in the Division of Pharmacoepidemiology.

An academic rank of Instructor or Assistant Professor at Harvard Medical School will be commensurate with experience, training and achievements. The successful candidate will collaborate and provide clinical informatics guidance in ongoing research projects, build their own research program, and engage in mentoring trainees.

QUALIFICATIONS:

Applicants should have excellent graduate training in biomedical informatics with research experience in clinical informatics. A beginning track record in clinical informatics research with a solid understanding in at least two of the following: natural language processing, feature engineering, machine learning, or missing data, is needed to be successful. Practical experience in working with large amounts of clinical data from electronic health records is a must. The application of clinical informatics tools to the study of the comparative effectiveness of medical products and interventions is a plus.

Candidates should hold a relevant Ph.D. degree or a medical degree, incl. M.D.,O.D., Pharm.D., plus a master’s degree with research experience.

WORK ENVIRONMENT:

Hybrid with in-person and remote activities in compliance with the facility and MA government guidelines. While in office, professional office environment, business casual. Working with a tight-knit, helpful, dedicated group of friendly people including over 20 Harvard Medical School faculty and 40 support members, including 8+ experienced programmers.

Employment at a Partners HealthCare System (“Partners”) affiliate is contingent upon:
- United States Citizenship and Immigration Services rules concerning identity and right to work in the United States
- Multi-state criminal background checks
- Pre-employment health and drug screening and annual compliance with the Influenza Vaccination Policy        

TO APPLY:
Please address a c.v. and a cover letter describing your interest to Sebastian Schneeweiss, M.D., Sc.D., Chief of the Division of Pharmacoepidemiology and Pharmacoeconomics and Professor of Medicine at Harvard Medical School, and submit via e-mail at lseton@bwh.harvard.edu.

EEO Statement: We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, pregnancy and pregnancy-related conditions or any other characteristic protected by law. Women and minority candidates are particularly encouraged to apply.

Primary Location: MA-Boston-BWH Boston Main Campus
Work Locations: BWH Boston Main Campus 75 Francis St  Boston 02115
Job: Research Investigator
Organization: Brigham & Women's Hospital(BWH)
Schedule: Full-time
Standard Hours: 40
Shift: Day Job
Posted Shift Description: Day / 40 hrs
Employee Status: Regular
Recruiting Department: BWH Department Of Medicine / Pharmocoepidemiology



Senior Programmer Analyst Machine Learning

Senior Programmer Analyst Machine Learning / Day/ 40 Hrs Pharmacoepidemiology & Pharmacoeconomics, DOM - (3190147)  

GENERAL SUMMARY/ OVERVIEW STATEMENT:

The Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital and Harvard Medical School was created to facilitate a wide range of activities related to the use and outcomes of medications. Its mission is to bring together the various specialties of medicine, epidemiology, biostatistics, health services research, and the social sciences to evaluate the effectiveness of existing and new prescription drugs in relation to their risks and costs; to study how medications are used by physicians and patients; and to develop methods to optimize prescription drug use. The machine-learning (ML) programmer/analyst will support this mission, working with a tight-knit, helpful, dedicated group of friendly people including over 20 Harvard Medical School faculty and 40 support members. The programmer will be responsible for loading, documenting, and cleaning large and complex health care data sets, creating analytical data files, and implementing statistical analyses with a focus on ML-learning models using Python and/or R (experience in both is desirable). The Programmer/Analyst will join a group of 8 experienced analysts.  Division research is particularly focused on the use of large, administrative healthcare databases, such as health insurance claims and electronic health records, but may also be based on surveys and randomized trials.

PRINCIPAL DUTIES AND RESPONSIBILITIES:
1. Works with statisticians and researchers to implement the appropriate data analyses in Python and/or R for a particular study.  Uses additional statistical packages (SAS, S-Plus, Stata, etc.), or other high level programming languages as appropriate.
2. Supports faculty and other research staff with the creation of analytic data files and the conduct of statistical analyses.
3. Responsible for loading, validating, documenting, managing, and analyzing data for both internal and collaborative research projects.
4. Responsible for assisting with data storage and management issues around both new and ongoing projects within the Division.
5. Responsible for working with other research staff within the Division to help specify appropriate analytical data files for the research question under investigation.
6. Responsible for creating and carefully documenting all derived analytical data files so that they can be understood and used by other members of the research team.
7. Documents and archives all aspects of analyses and data management.
8. Participate in monthly programmer team meetings.
9. Other responsibilities as assigned by the supervisor.

QUALIFICATIONS:
Proficiency with Python and/or R. Minimum Bachelors.

SKILLS/ ABILITIES/ COMPETENCIES REQUIRED:
- Attention to detail
- Ability to manage and prioritize multiple projects
- Ability to work effectively as a member of a multidisciplinary research team

ADDITIONAL DESIRED SKILLS; these skills are considered a plus but are not required
- Experience with SAS/STAT  procedures
- Experience working in a UNIX environment
- Experience with health care claims data
- Experience with relational databases
- Graduate education in epidemiology, biostatistics, or computer science

EEO Statement:
BWH is an Affirmative Action Employer.   By embracing diverse skills, perspectives and ideas, we choose to lead. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, national origin, sex, age, gender identity, disability, sexual orientation, military service, genetic information, and/or other status protected under law. We will ensure that all individuals with a disability are provided a 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.

Primary Location: MA-Boston-BWH Boston Main Campus
Work Locations: BWH Boston Main Campus 75 Francis St  Boston 02115
Job: Business and Systems Analyst
Organization: Brigham & Women's Hospital (BWH)
Schedule: Full-time
Standard Hours: 40
Shift: Day Job
Posted Shift Description: Day / 40 hrs
Employee Status: Regular
Recruiting Department: BWH Department Of Medicine / Pharmocoepidemiology



Research Fellow Geriatric + Causal Inference Development

Research Fellow Geriatric + Causal Inference Development/ Day/ 40 Hrs Pharmacoepidemiology & Pharmacoeconomics, DOM - (3178409)  

GENERAL SUMMARY/ OVERVIEW STATEMENT:

The Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital Department of Medicine and Harvard Medical School (the Division) is accepting applications for several postdoctoral fellows in pharmacoepidemiology. The Division is a 100-member interdisciplinary research center that brings together the various specialties of medicine, epidemiology, biostatistics, health services research, legal, regulatory and the social sciences to evaluate the effectiveness of prescription drugs in relation to their risks and costs; to study how medications are prescribed and used; to develop methods to optimize prescription drug use; to understand how medicines are approved and regulated after their marketing.

We are seeking one or more self-motivated, diligent, and independent fellows to work with Division faculty in one or more of the following areas:

1. Answering high impact questions to inform clinical decision making on the comparative effectiveness and safety of medications in the geriatric pharmacoepidemiology by applying and advancing cutting edge methods: Collaborate closely with Division faculty who are leaders in the field of geriatric pharmacoepidemiology. A fellow working in this area will answer critical clinical questions on the prescribing and deprescribing of medications and their comparative effectiveness and safety in older adults leveraging real world data, including administrative claims, electronic health records, and a variety of clinical assessment files, with the opportunity to lead several important research studies each year. The ideal candidate would be a team player and have a doctoral degree in epidemiology, aging research, or clinical geriatrics.  Having a clinical background or a degree in medicine combined with epidemiology training is desirable.

2. Developing cutting edge tools for valid causal inference incorporating machine learning and deep learning methods in combining electronic health records (EHRs) and claims data: A fellow working in this area will lead a series of studies aimed at expanding the capacity of machine learning methods to make causal inference in comparative effectiveness research in a semi-automated and data-adaptive fashion. Division faculty have access to multiple large-scale datasets that link longitudinal claims data with EHR data, including both structured data and free-text clinical notes and reports. Opportunities for both methodological and applied epidemiological research are available. Specific topic areas include, but are not limited to: data-adaptive high-dimensional causal inference analytics applying machine learning and deep learning methods to claims and EHR data; and natural language processing of unstructured data for confounding adjustment, risk profiling, and patient phenotyping.

Fellows will have an appointment at Harvard Medical School, receive close mentorship from faculty members in the Division, and engage in one or more projects intended to advance their careers in Geriatric pharmacoepidemiology or Causal inference method development research. Fellows will be highly encouraged to publish the results of their research during the appointment period. This opportunity is suited to individuals who are both independently motivated and collaborative and who thrive in a vibrant research environment working as part of a large team of experienced faculty and staff. Fellows must be comfortable giving and receiving feedback and integrating this feedback into their work. Fellows must enjoy recognizing the ideas and contributions of their colleagues and be comfortable being transparent in their work and decision making. Please see division website for more information on faculty and research topics: http://www.drugepi.org/.

PRINCIPAL DUTIES AND RESPONSIBILITIES:

The duties and responsibilities will vary depending on the specific topic area in which the fellow works, but will generally include:
1. Researching, developing, designing, executing, and interpreting epidemiologic studies in the specific topic areas.
2. Collaborating with methodologic and clinical colleagues on applied and/or methodological studies.
3. Investigating, creating, and applying new methods and technologies for research advancement in the specified topic areas.
4. Contributing to the scientific literature by way of reports, journals articles, and presentations.

Work environment:

Hybrid with in-person and remote activities in compliance with the facility and MA government guidelines.  While in office, professional office environment, business casual. Working with a tight-knit, helpful, dedicated group of friendly people including over 20 Harvard Medical School faculty and 40 support members, including  of 8+ experienced programmers.

Employment at a Partners HealthCare System (“Partners”) affiliate is contingent upon:
- United States Citizenship and Immigration Services rules concerning identity and right to work in the United States
- Multi-state criminal background checks
- Pre-employment health and drug screening and annual compliance with the Influenza Vaccination Policy            
QUALIFICATIONS:

Applications are invited from researchers with doctoral degrees (PhD/ScD/DrPH, MD, PharmD, or equivalent) and strong research and publication records in epidemiology, statistics, bioinformatics, or clinical medicine. Candidates are expected to have experience analyzing healthcare data (e.g., claims, EHR). Strong programming skills are highly desirable, depending on the specific topic areas of interest (e.g., R, Python, SAS).

SKILLS/ ABILITIES/ COMPETENCIES REQUIRED:
- Outstanding team player.
- Strong research design and analytical skills.
- Meticulous in all aspects of their work.
- Excellent time management and organizational skills.
- Ability to thrive in a dynamic environment and to adapt to shifting priorities, demands, and timelines.
- Strong written and oral communication skills.
- Strong programing skills are highly desirable and a willingness to learn new methods and tools relevant to their research is a must.

EEO Statement:
BWH is an Affirmative Action Employer.   By embracing diverse skills, perspectives and ideas, we choose to lead. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, national origin, sex, age, gender identity, disability, sexual orientation, military service, genetic information, and/or other status protected under law. We will ensure that all individuals with a disability are provided a 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.

Primary Location: MA-Boston-BWH Boston Main Campus
Work Locations: BWH Boston Main Campus 75 Francis St  Boston 02115
Job: Research - Other
Organization: Brigham & Women's Hospital(BWH)
Schedule: Full-time
Standard Hours: 40
Shift: Day Job
Posted Shift Description: Day / 40 hrs
Employee Status: Regular
Recruiting Department: BWH Department Of Medicine / Pharmocoepidemiology

Apply Here



Research Fellow Geriatric + Causal Inference Development

Research Fellow Geriatric + Causal Inference Development/ Day/ 40 Hrs Pharmacoepidemiology & Pharmacoeconomics, DOM - (3178395)  

GENERAL SUMMARY/ OVERVIEW STATEMENT:

The Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital Department of Medicine and Harvard Medical School (the Division) is accepting applications for several postdoctoral fellows in pharmacoepidemiology. The Division is a 100-member interdisciplinary research center that brings together the various specialties of medicine, epidemiology, biostatistics, health services research, legal, regulatory and the social sciences to evaluate the effectiveness of prescription drugs in relation to their risks and costs; to study how medications are prescribed and used; to develop methods to optimize prescription drug use; to understand how medicines are approved and regulated after their marketing.We are seeking one or more self-motivated, diligent, and independent fellows to work with Division faculty in one or more of the following areas:

1. Answering high impact questions to inform clinical decision making on the comparative effectiveness and safety of medications in the geriatric pharmacoepidemiology by applying and advancing cutting edge methods: Collaborate closely with Division faculty who are leaders in the field of geriatric pharmacoepidemiology. A fellow working in this area will answer critical clinical questions on the prescribing and deprescribing of medications and their comparative effectiveness and safety in older adults leveraging real world data, including administrative claims, electronic health records, and a variety of clinical assessment files, with the opportunity to lead several important research studies each year. The ideal candidate would be a team player and have a doctoral degree in epidemiology, aging research, or clinical geriatrics.  Having a clinical background or a degree in medicine combined with epidemiology training is desirable.

2. Developing cutting edge tools for valid causal inference incorporating machine learning and deep learning methods in combine electronic health records (EHRs) and claims data: A fellow working in this area will lead a series of studies aimed at expanding the capacity of machine learning methods to make causal inference in comparative effectiveness research in a semi-automated and data-adaptive fashion. Division faculty have access to multiple large-scale datasets that link longitudinal claims data with EHR data, including both structured data and free-text clinical notes and reports. Opportunities for both methodological and applied epidemiological research are available. Specific topic areas include, but are not limited to: data-adaptive high-dimensional causal inference analytics applying machine learning and deep learning methods to claims and EHR data; and natural language processing of unstructured data for confounding adjustment, risk profiling, and patient phenotyping.

Fellows will have an appointment at Harvard Medical School, receive close mentorship from faculty members in the Division, and engage in one or more projects intended to advance their careers in Geriatric pharmacoepidemiology or Causal inference method development research. Fellows will be highly encouraged to publish the results of their research during the appointment period. This opportunity is suited to individuals who are both independently motivated and collaborative and who thrive in a vibrant research environment working as part of a large team of experienced faculty and staff. Fellows must be comfortable giving and receiving feedback and integrating this feedback into their work. Fellows must enjoy recognizing the ideas and contributions of their colleagues and be comfortable being transparent in their work and decision making.

PRINCIPAL DUTIES AND RESPONSIBILITIES:

The duties and responsibilities will vary depending on the specific topic area in which the fellow works, but will generally include:
1. Researching, developing, designing, executing, and interpreting epidemiologic studies in the specific topic areas.
2. Collaborating with methodologic and clinical colleagues on applied and/or methodological studies.
3. Investigating, creating, and applying new methods and technologies for research advancement in the specified topic areas.
4. Contributing to the scientific literature by way of reports, journals articles, and presentations.

SKILLS/ ABILITIES/ COMPETENCIES REQUIRED:

- Outstanding team player.
- Strong research design and analytical skills.
- Meticulous in all aspects of their work.
- Excellent time management and organizational skills.
- Ability to thrive in a dynamic environment and to adapt to shifting priorities, demands, and timelines.
- Strong written and oral communication skills.
- Strong programing skills are highly desirable and a willingness to learn new methods and tools relevant to their research is a must.

QUALIFICATIONS:
Applications are invited from researchers with doctoral degrees (PhD/ScD/DrPH, MD, PharmD, or equivalent) and strong research and publication records in epidemiology, statistics, bioinformatics, or clinical medicine. Candidates are expected to have experience analyzing healthcare data (e.g., claims, EHR). Strong programming skills are highly desirable, depending on the specific topic areas of interest (e.g., R, Python, SAS).

WORKING CONDITIONS:
Hybrid with in-person and remote activities in compliance with the facility and MA government guidelines.  While in office, professional office environment, buisiness casual. Working with a tight-knit, helpful, dedicated group of friendly people including over 20 Harvard Medical School faculty and 40 support members, including  of 8+ experienced programmers.

EEO Statement:
BWH is an Affirmative Action Employer.   By embracing diverse skills, perspectives and ideas, we choose to lead. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, national origin, sex, age, gender identity, disability, sexual orientation, military service, genetic information, and/or other status protected under law. We will ensure that all individuals with a disability are provided a 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.

Primary Location: MA-Boston-BWH Boston Main Campus
Work Locations: BWH Boston Main Campus 75 Francis St  Boston 02115
Job: Research - Other
Organization: Brigham & Women's Hospital(BWH)
Schedule: Full-time
Standard Hours: 40
Shift: Day Job
Posted Shift Description: Day / 40 hrs
Employee Status: Regular
Recruiting Department: BWH Department Of Medicine / Pharmocoepidemiology

Apply Here


Diversity in Pharmacoepidemiology Summer Internship Program

Title: Diversity in Pharmacoepidemiology Summer Internship Program 

General Summary and Overview:
The Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital(BWH) and Harvard Medical School (HMS) invites rising college senior students to participate in an eight-week summer internship program that provides training and skills development in public health research about prescription medications. The long-term goal of the program is to assist students whose backgrounds have historically been underrepresented in biomedical and clinical research to build necessary skills for their career interests in public health, medicine, and other related fields. Students self-identifying as Blacks or African Americans, Hispanics or Latinos, American Indians or Alaska Natives, Native Hawaiians, and other Pacific Islanders are eligible to apply. US Citizens & Permanent Residents and International students who are matriculated at U.S. colleges/Universities may apply (visa restrictions may apply).

Learning Outcomes:

•Experiential and didactic learning through division meetings, including journal club sessions, webinars, and guest presentations
• Completion of a summer capstone project under the supervision of a faculty member with the opportunity to present findings to faculty
•State-level public policies on prescription drug costs, pricing, and access
• Mentoring through career development meetings, one-on-one faculty and intern meetings, clinical shadowing, and strategic, long-term mentor/mentee pairing to guide further career planning
•Opportunity to audit summer courses offered by faculty at Harvard T.H Chan School of Public Health and in other Harvard venues

Eligibility:

The fellowship program is best suited for rising senior undergraduate students with an interest in public health, epidemiology, medicine, pharmacy, biostatistics and/or health services research and policy, who are enrolled in a four-year degree program and who self-identify as Black/African American, Native American, Alaskan Native and/or Hispanic/Latinx. Prior research experience is not required, but applicants must be able to convey an interest in research and how this program will help them to achieve their long-term career goals. Quantitative coursework, skills, or experience is preferred, but not required. 

Work environment: Professional Office Environment, Business Casual.  

Application Link: https://forms.office.com/r/XhXif4WbQW

Application Deadline: March 15, 2022 at 11:59 PM ET

Internship Duration: Monday, June 20th, 2022 -Friday, August 12th, 2022 (8 weeks)

Contact Information: pharmacoepi_intern@bwh.harvard.edu 

EEO Statement:
As a not-for-profit organization, Mass General Brigham is committed to supporting patient care, research, teaching, and service to the community. We place great value on being a diverse, equitable and inclusive organization as we aim to reflect the diversity of the patients we serve. At Mass General Brigham, we believe in equal access to quality care, employment and advancement opportunities encompassing the full spectrum of human diversity: race, gender, sexual orientation, ability, religion, ethnicity, national origin and all the other forms of human presence and expression that make us better able to provide innovative and cutting-edge healthcare and research.

Post-doctoral Fellowships in Pharmacoepidemiology:

Sebastian Schneeweiss, MD, MS, ScD
Chief, Division of Pharmacoepidemiology and Pharmacoeconomics
Professor of Medicine, Harvard Medical School
Professor in Epidemiology, Harvard Chan School of Public Health


Department of Medicine
Brigham and Women’s Hospital
1 Brigham Circle, Suite 3030, Boston, MA 02120
P: 617 278-0930 M: 617331-2632
sschneeweiss@bwh.harvard.edu
www.DrugEpi.org

April 5, 2021

The Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital Department of Medicine and Harvard Medical School is accepting applications for multiple post-doctoral fellowships in pharmacoepidemiology both in applied or methodologically focused research. 

The Division includes 25 faculty (drugepi.org/dope/team) and 75 staff who work closely together to research how we use medications effectively and improve health. We are a world-leading interdisciplinary research center that brings together the various specialties of medicine, epidemiology, biostatistics, health services research, legal, regulatory and the social sciences to evaluate the effectiveness of prescription drugs in relation to their risks and costs; to study how medications are prescribed and used; to develop methods to optimize prescription drug use; to understand how medicines are approved and regulated after their marketing. The Division is a first-rank training site for graduate students and fellows in a variety of subject areas and methodological research. We are seeking one or more self-motivated, diligent, and independent fellows to work with Division faculty in one or more of the following areas: 

  • Developing and implementing cutting-edge methods to bridge the gap between randomized clinical trials (RCTs) and real-world evidence (RWE): RCTs and RWE are critical and complementary sources of evidence generation about the benefits and safety of medical products. A fellow working in this area will be involved in several interrelated projects that will leverage individual-level RCT data to explore this complementarity and will be expected to explore and test novel analytical approaches for analysis of RCT and real-world data. Training and experience in statistical modeling and programing is required. Experience with developing prediction models, model validation and calibration approaches, imputation methods, Monte Carlo simulations, and machine learning algorithms is highly desirable. 
  • Answering high-impact questions to inform clinical decision making on the comparative effectiveness and safety of medications in cardio-metabolic and renal conditions by applying and advancing cutting edge methods: A fellow working in this area will collaborate closely with Division faculty who are leaders in the pharmacoepidemiology of cardio-metabolic and renal diseases to answer critical clinical questions on the use of medications and their comparative effectiveness and safety leveraging real-world data, including administrative claims, electronic health records, and clinical registries. Fellows will have the opportunity to lead several important research studies. The ideal candidate would be a team player and have a doctoral degree in pharmacoepidemiology and ideally a clinical background, or a degree in medicine combined with pharmacoepidemiology/ epidemiology training. 
  • Developing cutting edge tools that improve causal inference by incorporating machine learning and deep learning methods using electronic health records (EHRs) and claims data: A fellow working in this area will lead a series of studies aimed at expanding the capacity of machine learning methods to make causal inference in comparative effectiveness research in a semi-automated and data-adaptive fashion. Division faculty have access to multiple large-scale datasets that link longitudinal claims data with EHR data, including both structured data and free-text clinical notes and reports. Opportunities for both methodological and applied epidemiological research are available. Specific topic areas include, but are not limited to: data-adaptive high-dimensional causal inference analytics applying machine learning and deep learning methods to claims and EHR data; and natural language processing of unstructured data for confounding adjustment, risk profiling, and patient phenotyping. 
  • Advancing and applying innovative methods for studying outcomes of drug-drug interactions in electronic healthcare data: Division members are actively working on methodological and substantive studies to advance evidence generation related to clinical outcomes of drug-drug interactions. Methodological work includes both innovative large-scale screening approaches and improvements in study design as applied to drug-drug interactions. Current applied work focuses on drug interactions with opioids as well as other substantive clinical areas, such as diabetes. The ideal candidate would have a doctoral degree in pharmacoepidemiology and ideally a clinical background, or a degree in pharmacy or medicine combined with pharmacoepidemiology/epidemiology training. 
  • Identifying risk factors that contribute to increased risk of opioid-related adverse events among older adults: Older adults are at high risk of opioid-related emergency visits and hospitalizations even when they use opioids as prescribed. A fellow working in this area will lead a series of studies aimed at identifying non-opioid medications that could be contributing to increased risk of opioid overdose and other opioid-related adverse events in geriatric population, using health insurance claims data (Medicare and commercial insurance databases) and advanced pharmacoepi methods. Opportunities to work with EHR data and for methodological research in this area will be available. The ideal candidate would have a doctoral degree in pharmacoepidemiology; training and experience in statistical modeling and programing is required. 

If you are interested in this opportunity or have any questions, contact Lewis Seton at lseton@bwh.harvard.edu.

RESEARCH ASSISTANT I

Research Assistant I / Day/ 40 Hrs Pharmacoepidemiology & Pharmacoeconomics, DOM - (3183100)  

GENERAL SUMMARY/ OVERVIEW STATEMENT:

Working under the direction of the Research Manager, Division Chief, and faculty of the Division of Pharmacoepidemiology and Pharmacoeconomics, the Research Assistant is responsible for the daily organization, coordination, and conduct of activities related to studies of the safe and effective use of medications in a variety of contexts. The Division’s research is neither clinical nor bench science, and is conducted in an office setting with minimal patient interaction. Please see division website for more information on faculty and research topics: http://www.drugepi.org/.

PRINCIPAL DUTIES AND RESPONSIBILITIES:

The typical Research Assistant’s duties are a combination of research and operational tasks. Research tasks include, but are not limited to, data management and manipulation (MS Excel, MS Access, statistical software), data entry relevant to studies and monitoring, inquiries on various policy initiatives, performing literature searches, generating graphs, figures and presentations, gathering research materials for ongoing and future research projects,  (i.e. manuscript and grant proposal writing), conducting interviews, assisting with conducting trials,  and other duties as needed. Operational tasks ensure that the work in the entire Division as well as individual projects run efficiently.
 
These include a variety project and office management elements (scheduling, meeting organization, website and mailing list support, general office support, administrative support). Research Manager coordinates and monitors assignments.

Assigned projects will vary in type and amount of work required as well as duration; assignments can be individual or shared with other Research Assistants on the team. Teamwork is highly supported and encouraged.  Prospective start date is Summer 2022, and a requires a minimum two-year commitment.

Work environment:

Professional Office Environment, Business Casual. Working as part of a tight-knit group of hardworking friendly  people—an internationally acclaimed research unit of 22 Harvard faculty, 65 support staff, and 9 Programmers.

Employment at a Partners HealthCare System (“Partners”) affiliate is contingent upon:
- United States Citizenship and Immigration Services rules concerning identity and right to work in the United States
- Multi-state criminal background checks
- Pre-employment health and drug screening and annual compliance with the Influenza Vaccination Policy            
QUALIFICATIONS:

BA/BS required. Strong attention to detail and ability to manage multiple priorities is a must. Competency with MS Excel, MS Word, MS PowerPoint is required; familiarity with SAS, R and/or Tableau are a plus but NOT a requirement. Prior social science or medical research experience is preferred, and epidemiology and/or biostatistics exposure is a plus. A strong applicant will display the ability to communicate with multiple levels of staff and work effectively with others in a team environment.

SKILLS/ ABILITIES/ COMPETENCIES REQUIRED:
- BA/BS degree
-Competency with MS Word, MS Excel, MS PowerPoint
-Familiarity with SAS, R, Python, SQL and/or Tableau is a plus but not required
- Ability to adapt to a fast-paced environment and learn quickly
- Teamwork and cooperation
- Attention to detail and excellent organizational skills
- Ability to manage multiple priorities
- Ability to use independent judgment
- Excellent communication and interpersonal skills, telephone and email etiquette          

EEO Statement:
We are an equal opportunity employer, and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.

Primary Location: MA-Boston-BWH Boston Main Campus
Work Locations: BWH Boston Main Campus 75 Francis St  Boston 02115
Job: Research - Other
Organization: Brigham & Women's Hospital(BWH)
Schedule: Full-time
Standard Hours: 40
Shift: Day Job
Posted Shift Description: Day / 40 hrs
Employee Status: Regular
Recruiting Department: BWH Department Of Medicine / Pharmocoepidemiology

Apply Here