Machine Learning Analytics Summer Intern

Remote: 
Full Remote
Contract: 
Work from: 

Offer summary

Qualifications:

Candidate for B.S in computer science or data science related field, Experience with Java, Python, SQL and related machine learning technology stack, Entry level knowledge of Dialysis industry helpful, but not required, Energetic, problem solver mindset, positive attitude, and enjoys working in teams..

Key responsibilities:

  • Assist in establishing machine learning algorithms to improve 90-day Home HD patient retention
  • Gather and organize data sources systematically to support retention risk score modeling
  • Support development of features from diverse data sets to advance algorithm accuracy
  • Execute exploratory data analysis for important retention algorithm initiatives.

Fresenius Medical Care North America logo
Fresenius Medical Care North America XLarge http://www.freseniusmedicalcare.com/
10001 Employees
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Job description

This is a full time summer internship starting in Mid-May 2025, ending Mid-Aug 2025.

INVENT. IMPROVE. INSPIRE.
Fresenius Medical Care is not your typical medical products company. We are a close-knit team of talented professionals driven by a shared commitment: to lead a movement to dramatically improve renal care with innovative yet simple therapeutic solutions that benefit patients, caregivers, and society. We pursue this goal through a passionate commitment to Innovate, Educate and Advocate for the breakthroughs that are redefining renal care. Additionally, Fresenius Medical Care works in community with its customers, patients, industry partners, and government leaders and agencies to realize the vision of better, simpler, more accessible renal care. 
 

           

Position Summary:

The selected intern will assist in driving retention and patient satisfaction initiatives as part of the Fresenius Medical Care Home Therapy Product Management team.  The role will report to the Director of Product Management, Home Therapies connected Health. The intern will work collaboratively with several cross-functional departments including Global Medical Office, Information Technology Solutions, Home Digital Value Stream, and other members of the Home Therapies Connected Health Product Management as part of this role.

Responsibilities:

  • Assist in establishing machine learning algorithms to improve 90-day Home HD patient retention
  • Assist in gathering and organizing the data sources in a systematic manner to support the retention risk score modeling
  • Support development of features from diverse data sets to advance the algorithms accuracy, specificity, sensitivity
  • Help with testing and validating the model at various stages of its evolution
  • Execute exploratory data analysis / profiling analysis for important retention algorithm initiatives
  • Provide support for our Global Medical Office and Information Technology Solution team members

Education & Qualifications:

  • Candidate for B.S in computer science or data science related field
  • Entry level knowledge of Dialysis industry helpful, but not required
  • Experience with Java, Python, SQL and related machine learning technology stack
  • Experience with AWS and other cloud platforms, at least familiarity is needed
  • Experience with SalesForce.com, SAP, but not required
  • Experience with data analytics and machine learning, required
  • Energetic, problem solver mindset, positive attitude, and enjoys working in teams
  • EO/AA Employer: Minorities/Females/Veterans/Disability/Sexual Orientation/Gender Identity

Fresenius Medical Care North America maintains a drug-free workplace in accordance with applicable federal and state laws.

If your location allows for pay/benefit transparency, please click the link below to request further information on this position. Pay Transparency Request Form (smartsheet.com)

EOE, disability/veterans

Required profile

Experience

Spoken language(s):
English
Check out the description to know which languages are mandatory.

Other Skills

  • Teamwork
  • Problem Solving

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