Data Scientist/Machine Learning Engineer !!! New York/ Atlanta (Onsite)

Postion : Traditional Data Scientist/Machine Learning Engineer

Location: New York & Atlanta, GA

Job Description:

Must haves: Classification Logistic Regression, Decision Tree, Random Forest

1. We are looking for someone who is well-versed with different supervised (Classification/Regression) and unsupervised/clustering ML models. He/She should understand how these model algorithms work. We are looking for hands-on experience around these predictive models (from data pre-processing to EDA to Modeling to Evaluation).

Classification Logistic Regression, Decision Tree, Random Forest

Regression Linear, Gradient Boosting, Neural Nets, KNN

Unsupervised K-means and other clustering techniques

2. Should have insurance domain experience (specifically Underwriting/Claims)

3. Should have developed propensity models within insurance business domain

4. Should be proficient at Python

5. Should have prior experience in AWS SageMaker, Databricks, etc.

6. Should be familiar with NLP techniques (OCR based text extraction, document classification, document summarization)

7. Palantir Foundry and AIP experience is a plus

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Postion : Traditional Data Scientist/Machine Learning Engineer

Location: New York & Atlanta, GA

Job Description:

Must haves: Classification Logistic Regression, Decision Tree, Random Forest

1. We are looking for someone who is well-versed with different supervised (Classification/Regression) and unsupervised/clustering ML models. He/She should understand how these model algorithms work. We are looking for hands-on experience around these predictive models (from data pre-processing to EDA to Modeling to Evaluation).

Classification Logistic Regression, Decision Tree, Random Forest

Regression Linear, Gradient Boosting, Neural Nets, KNN

Unsupervised K-means and other clustering techniques

2. Should have insurance domain experience (specifically Underwriting/Claims)

3. Should have developed propensity models within insurance business domain

4. Should be proficient at Python

5. Should have prior experience in AWS SageMaker, Databricks, etc.

6. Should be familiar with NLP techniques (OCR based text extraction, document classification, document summarization)

7. Palantir Foundry and AIP experience is a plus

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.