Description

The Principal Data Scientist uses mathematics, statistics, modeling, business analysis, and technology to transform high volumes of complex data into advanced analytic solutions. The Principal Data Scientist provides strategic advice and guidance to functional team(s). Highly skilled with broad, advanced technical experience.

Responsibilities

Humana, Inc is seeking a Principle Data Scientist (Director-level expereience) who can bring their expertise in emerging AI areas ( NLP, computer vision in particular) to join their growing team in our new location in Boston, Massachusett's - Studio H, located at 281 Summer Street in the Seaport District.

At Humana Studio H, we offer the expansive data and technology architecture needed to get things done. We’re harnessing the power and potential of predictive modeling, artificial intelligence and machine learning to revolutionize—and forever impact—the way healthcare is delivered.

We’re building a new healthcare experience that puts customers in the center and helps people achieve their best health.  And we’re proud of our diverse backgrounds and experiences that create a fertile ground for innovation and progress. We collaborate flexibly, fearlessly.
 

The Principal Data Scientist is a Director-level position here at Humana. This role will provide thought leadership on the practical application of machine learning and advanced analytical methods and cultivate a data driven culture across the company. You will collaborate with key business leaders to understand business problems and formulate analytical solutions for problem solving as well as lead or collaborate with a team of data scientists, data analysts or big data engineers on analytical projects, proving benefits of solutions and helping with execution and measurement.

In addition, this role will be expected to:

• Have absolute fun with the rest of us,
• Develop right analytical solutions from a diverse repertoire of analytical techniques,
• Apply solutions tool agnostic ,
• Mentor junior talent,
• Challenge & be challenged  by the team and leadership.

Required Qualifications

• Bachelor's Degree and 10+ years technical experience OR Master’s Degree and 6+ years technical experience, such as Computer Science, Mathematics or Statistics and/or related field
• 2+ years project and/or people leadership experience

• Experience with state-of-the-art techniques in machine learning algorithms, including deep neural networks, NLP, dimensionality reduction, ensemble methods, graph algorithms

• Extensive experience solving analytical problems using quantitative and qualitative approaches. Specifically, demonstrated practical experience in building end to end data science solutions and applying machine learning methods to real world problems.
• Strong programming skills in at least one language such as Python, Java, Scala or R.
• Experience with providing strategic advice and guidance to functional teams in a collaborative fashion; working alongside other analytic individuals and teams
• Experience in developing, maintaining, and collecting structured and unstructured data sets for analysis and reporting
• Has experience with Deep Learning models, AI development, agile software launching, Tensorflow experience, and/or deploying model pipelines across various technologies, such as Kubernetes or Docker.
• Understanding of hardware to enable various Data Science technologies (i.e. GPU application in Deep Neural Network development)
• Successful demonstrated experience in working with senior executives to develop and drive segment or enterprise-wide functional strategies
• Ability to advise one or more areas, programs or functions
• Ability to work at very high levels in multiple knowledge and functional areas across the enterprise
• Experience leading and growing junior talent
• Experience with disrupting or leading a wave of change in their industry

Preferred Qualifications

• 3-5+ years project and/or people leadership experience
• Ph'd strongly preferred
• Big data experience (Hive, Spark, HDFS) a big plus!
• Experience in GCP, AWS, Azure or other cloud ecosystems.