Data Scientist - Advisor

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

CVS Health is the nation's premier health innovation company helping people on their path to better health. We are building a new health care model that is easier to use, less expensive, and puts the consumer at the center of their care. Data Science is one of the foundational pillars driving CVS Health into a data centric company. You will be part of a large group of highly accomplished data scientists to help build innovative solutions and manage data using the latest tools and technology. Whether you specialize in Machine Learning, Optimization & Simulations, Statistical Analysis, or Natural Language Processing, you will have ample opportunity to contribute, grow and achieve your best working for Fortune 5’s largest healthcare company.

Who we are:

CVS Health Enterprise Analytics organization has several teams, Retail Analytics, Pharmacy Personalization Analytics and Data Strategy and Engineering, helping lead the effort to transform healthcare and provide better outcomes for patients by leveraging solutions in a particular business analytic area and deliver new insights to leaders that manage the roadmap for enhancements that drive new development on existing or new solutions.

What you will do:

The Data Scientist role will be a key contributing member of the Pharmacy Personalization team responsible for translating business problems into clear analytical/data science oriented goals and deliver against them. You will be part of a collaborative, high-performing team that leverages cutting edge machine learning techniques across regression/classification, deep learning, NLP and causal inference to deliver direct impact to the bottom & top line of the Pharmacy growth business unit.

The role will be directly responsible for:

• Developing machine learning and other advanced models that drive business outcomes across enterprise
• Leading discussions with key internal and external stakeholders to determine how best to leverage machine learning and advanced analytic methods to support business objectives
• Applying appropriate modeling approaches for a given analytic problem (machine learning methods ranging from regression methods, decision trees, deep learning, NLP techniques, uplift modeling; operations research; statistical modeling such as multivariate techniques)
• Partnering with Data Engineering peers in identifying and designing core modeling pipelines that will ensure scalable deployment of ML models
• Providing subject matter expertise in analyzing underlying statistical concepts and computational approaches that enable efficient execution of models and may be able to design and implement modifications and enhancements to the computations
• Contributing to the wider Enterprise data science community by sharing their subject matter expertise, participating in journal clubs, recruiting the next wave of talent etc.
• Presenting their work to both technical and non-technical audience in a coherent way to further influence decision making using a analytics driven approach

  Required Qualifications

  Bachelor’s Degree with 5+ years, or Master’s with 3+ years or PhD with professional experience in any of the following fields:

  Computer Science, Data Science, Applied Math, Engineering, Operation Research, Statistics, Epidemiology, or other related quantitative fields.

  Professional Experience includes the following areas:

• Hands-on experience designing and implementing end-to-end solutions using Machine Learning in a fast-paced environment
• Expert level proficiency with common modeling tools and frameworks, e.g. Python, R, Scala, TensorFlow, etc.
• Proficient with model pipeline technologies like Airflow, MLFlow, MLOps, Kubeflow etc.
• Proficiency with tools to automate CI/CD pipelines (e.g., Jenkins, GIT, Control-M)
• Hands-on experience manipulating data on big data environments using state of the art technologies like PySpark, Hive etc.
• Experience with cutting edge ML tools & languages, both open source and proprietary eg.TensorFlow, PyTorch, PySpark etc.
• Excellent communication skills and adept at explaining complex technical concepts to both technical and non-technical audiences. Ability to influence technical decision by leveraging their in-depth Machine Learning/Statistics and data expertise
• Ability to understand data management and model productionalization best practices
• Experience data visualization that help tell a deeper story of data distributions and improve explainability of the models.

  Preferred Qualifications

• Experience with cloud technologies like MS Azure, GCP, AWS etc
• Productionalized one or more ML applications on big data servers

  Education

  Bachelor’s Degree with 5+ years, or Master’s with 3+ years or PhD with professional experience in any of the following fields:

  Computer Science, Data Science, Applied Math, Engineering, Operation Research, Statistics, Epidemiology, or other related quantitative fields.