Data Scientist II - ML in R

This is a hybrid 3 days on-site Tue, Wed, Thurs.

The expectations for a Data Scientist II at the client include the ability to analyze and interpret large, complex datasets using advanced statistical and machine learning techniques, collaborate effectively with cross-functional teams to solve business problems, develop and deploy predictive models, automate and scale analytical workflows, and clearly communicate insights to diverse stakeholders. Strong technical skills are expected—such as proficiency in Python or R, expertise with data visualization tools, familiarity with cloud and MLOps practices, and an understanding of data privacy and healthcare compliance.

Additionally, staying up to date with industry trends and demonstrating initiative in problem-solving and continuous learning are important for success in this role.

• Excellent understanding of machine learning techniques and algorithms, such as Logistic Regression, SVM, Random Forests, Deep Learning etc.
• Analyze and understand large amounts of data to determine suitability for use in models and then work to segment the data, create variables, build models, and test those models.
• Expertise in transforming business requirements into analytical models, designing algorithms, building models, developing Data Mining and Reporting Solutions that scales across massive volume of Structured and Unstructured Data
• Extensive experience in creating python scripts for data integration from APIs and web scrapping to structured and unstructured databases
• Expertise in cloud computing environments (e.g., AWS, Azure, GCP) for scalable data processing and deployment of models.
• Experience in building and optimizing deep learning models using frameworks such as Tensor Flow or PyTorch.
• Advanced skills in SQL, databases (relational and No
  
  SQL), and data engineering (ETL pipelines, data warehousing).
• Proficiency in deploying machine learning models as APIs or services using tools like Docker, Kubernetes, or Flask.
• Familiarity with CI/CD pipelines and MLOps best practices for model management and reproducibility.
• Strong background in statistical modeling, algorithm selection, and experimental design.
• Experience with NLP Techniques, text mining, and computer vision applications relevant to healthcare.
• Knowledge of reproducible research tools (Jupyter notebooks, Git version control).
• Data security, privacy, and compliance understanding, especially relevant in healthcare and pharma.

• 401(k)
• Medical, Dental and Vision Insurance
• Term Life Insurance
• Accidental Death and Dismemberment
• Long Term Disability