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flaglerhealth

Senior Data Scientist

FlaglerHealth seeks a highly skilled Senior Data Scientist to join our Engineering team. The primary mission of this role is leveraging advanced statistical methods and machine learning techniques to solve complex problems within healthcare technology. You will be responsible for the entire data science lifecycle: from ingesting disparate, high-volume datasets (EHRs, claims data) to prototyping, validating, and deploying predictive models into production systems. Key responsibilities include feature engineering, building sophisticated NLP models for clinical text, optimizing model performance metrics, and collaborating with domain experts to translate raw data patterns into actionable business intelligence. A strong academic background combined with practical experience in large-scale ML deployments is essential.

flaglerhealth NYC Office, United States

(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)

Preparing

2800

Question set

300

Duration

Permanent

Focus areas

Data ScienceMachine LearningPredictive ModelingStatistics

People Also Ask (FAQs)

What is the last date to apply for Senior Data Scientist?

The closing date is listed in the official advertisement — apply as early as possible.

How do I apply for Senior Data Scientist?

Use the official apply link on this page to submit your application. A step-by-step how-to-apply guide for this job is also linked below.

Is there negative marking in the flaglerhealth test?

The marking rule is announced per test in the advertisement and roll-number-slip instructions. Many MCQ screening tests have no negative marking, but always verify it for your specific test.

What is the passing marks for this test?

The official passing marks are generally 50%, but merit is competitive — scoring above 75% is a safer target to secure an interview call.

How can I prepare for the Senior Data Scientist test?

Prepare with the free subject-wise MCQs, past papers and timed mock tests on PrepPro Academy — every question includes the answer with an explanation. Focus areas: Data Science, Machine Learning, Predictive Modeling, Statistics.

Quick start

Review the job summary, then jump into prep with the same flow used for exam starts.

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