Easter Seals Jobs

Description

The digital mental health team at Boehringer is seeking a post-doctoral research fellow to help generate actionable evidence from the use of digital health technologies & therapeutics to accelerate clinical development in mental health.

As an employee of Boehringer Ingelheim, you will actively contribute to the discovery, development, and delivery of our products to our patients and customers. Our global presence provides opportunity for all employees to collaborate internationally, offering visibility and opportunity to directly contribute to the companies' success. We realize that our strength and competitive advantage lie with our people. We support our employees in several ways to foster a healthy working environment, meaningful work, diversity and inclusion, mobility, networking, and work-life balance. Our competitive compensation and benefit programs reflect Boehringer Ingelheim's high regard for our employees.

Duties & Responsibilities

• Under supervisor/s guidance, the post doc will focus on independent research and data analysis of ongoing or completed clinical trials in mental health along with manuscript preparation and submissions

• Conduct statistical analysis of multimodal clinical trial data to assess various outcomes of interest (e.g., patient treatment response, disease progression, adherence, data quality, patient phenotypes/stratification)

• Proactively lead the development of reusable and reproducible data analysis pipelines for extracting key quality metrics, features from multimodal clinical trial data

• Collaborate with an interdisciplinary and cross functional teams communicating/presenting research findings through presentations and scientific reports

Requirements

• PhD from an accredited school earned before the start date at Boehringer Ingelheim. Preferred degree of study is in quantitative field such as biomedical engineering statistics computer science electrical engineering or a related field

• Demonstrable proficiency in at least one of the scripting languages Python or R preferred and experience writing reusable managed Git and well documented code

• Demonstrated scientific experience in digital health related projects as measured by publications and presentations

• Strong background in applied data science signal processing and machine learning stats preferably in health care domain

• Experience in analyzing temporal health related data from digital health apps, wearables or smartphones sensors (eg. accelerometer, EEG, patient voice samples) is strongly preferred

• Excellent interpersonal communication and presentation skills and ability to work in a dynamic and collaborative environment

• Experience in working with clinical trial data and or mental health is an asset but not a requirement for this position

Eligibility Requirements :

• Must be legally authorized to work in the United States without restriction.

• Must be willing to take a drug test and post-offer physical (if required).

• Must be 18 years of age or older.

Duration:

Two Years

Location:

Remote (within US)

Application Instructions

1. Curriculum vitae

2. Letter of intent - A summary of your past relevant research experience along with a brief summary of how this fellowship at Boehringer Ingelheim can help further your career growth. *Please upload under My Documents, Additional Attachments.

COMPENSATION:

This position offers a base salary of $80,000. The position may be eligible for a role specific variable or performance-based bonus and or other compensation elements.  For an overview of our benefits please click here (https://www.boehringer-ingelheim.com/us/careers/benefits-rewards) .

All qualified applicants will receive consideration for employment without regard to a person’s actual or perceived race, including natural hairstyles, hair texture and protective hairstyles; color; creed; religion; national origin; age; ancestry; citizenship status, marital status; gender, gender identity or expression; sexual orientation, mental, physical or intellectual disability, veteran status; pregnancy, childbirth or related medical condition; genetic information (including the refusal to submit to genetic testing) or any other class or characteristic protected by applicable law.