Senior Data Scientist

CVS Health•New York, NY

4d•$111,240 - $222,480•Hybrid

About The Position

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Aetna Resources LLC, a CVS Health company, is hiring for the following role in New York, NY: Sr. Data Scientist to lead and participate in the development, validation and delivery of algorithms, statistical models and reporting tools. Solves complex analytical problems. Hybrid Position: remote work permitted but must live within commuting distance of designated office location and remain available to report to office as needed and required. Multiple positions.

Requirements

Master’s degree (or foreign equivalent) in Computer Science, Data Science, Statistics, Business Analytics, Computer Science and Engineering, or a related field and two (2) years of experience in the job offered or related occupation.
Two (2) years of experience in Programming in SQL, Python, or R;
Two (2) years of experience in Docker, Kubernetes, JIRA, Rally, or Confluence;
Two (2) years of experience in Hive, Cassandra, Pig, MySQL or NoSQL;
Two (2) years of experience in PowerBI or Tableau;
Two (2) years of experience in Machine learning, statistical analysis, and predictive modeling;
Two (2) years of experience in Domain support for healthcare or retail organization;
Two (2) years of experience in Relational database concepts;
Two (2) years of experience in Contributing to largescale applications development, data science, or data analytics projects;
Two (2) years of experience in Data analytics on large data sets in healthcare, business, or retail sector;
Two (2) years of experience in Designing data models and solutions for analytical and reporting use cases;
Two (2) years of experience in Machine learning algorithms;
Two (2) years of experience in End-to-end data science lifecycle/workflow;
Two (2) years of experience in Developing data and analytics-based platform capabilities, services, or products;
Two (2) years of experience in Supporting large data, analytics, and technology modernization initiatives;
Two (2) years of experience in Feature engineering, model training, hyperparameter tuning, distributed model training, and supervised and unsupervised learning implementation;
Two (2) years of experience in Quantitative analysis techniques, including clustering, regression, and pattern recognition.

Responsibilities

Designs systems that utilize data-oriented programming languages and visualization software to explore, analyze, and interpret large volumes of data in various forms and solve complex business problems.
Defines data science initiatives by utilizing data mining, data modeling, natural language processing, and machine learning techniques to extract data from multiple sources and deliver predictive models that inform solutions.
Designs and develops data solutions that meet client requirements and organizational quality standards by leveraging industry-leading tools, technologies, and best practices to profile and translate data.
Applies data science expertise and machine learning knowledge to improve patient care capabilities, optimize healthcare operations, and drive evidence-based decision-making.
Develops and implements analytical frameworks, key metrics, and performance dashboards to monitor, measure, and evaluate healthcare quality, efficiency, and performance.
Collaborates with stakeholders across the organization, including executives, clinicians, Information Technology (IT) professionals, and business analysts, to understand their needs and provide data-driven solutions.
Delivers presentations and consultative services to existing and prospective constituents on data analytics results and solutions.
Improves professional data science knowledge by staying up to date on innovative data analysis techniques, visualization tools and software, and object-oriented programming languages.
Helps establish and implement programs that ensure departmental adherence to all applicable data governance policies and procedures by educating team members on data science best practices.