Lead Data Scientist from Collabera

Client – Health Insurance 

Job Title – Data Scientist 

Location – Herndon, VA – 20191 

Duration – 12 Months (Possible Conversion / Extension) 

About the Role: 

• The role of a Machine Learning and Artificial Intelligence Lead involves leading the proliferation of machine learning and artificial intelligence solutions throughout the enterprise. 


• The individual will be responsible for identifying and solving business problems using various numerical techniques, algorithms, and models in statistical modeling, machine learning, operations research, and data mining. 


• They will also support data science initiatives by applying advanced analytical capabilities and communicating with product teams and customers. 

Responsibilities: 

• Lead all data mining and extraction activities and apply algorithms to derive insights. 


• Synthesize analytical findings for consumption by teams and senior executives. 


• Lead the proliferation of machine learning and artificial intelligence solutions. 


• Apply artificial intelligence techniques to achieve concrete business goals while managing limited resources and constraints around data. 


• Mentor and develop junior data scientists for advanced data analysis. 


• Translate business priorities and create data science deliverables. 


• Lead the implementation of AI/DS best practices for new data products and build robust and scalable software. 

Education Qualification: 

• Bachelor's Degree in Statistics, Mathematics, Computer Science, or a related field. 

Required Skills: 

• 8 years of relevant work experience. 


• Experience building data pipelines for models. 


• Experience optimizing model code and deployments. 


• Experience with Cloud Platforms, specifically AWS ecosystem. 


• Proficiency in Data Engineering & Data Science Tools. 


• Ability to communicate effectively and document objectives and procedures. 


• Ability to leverage a wide variety of data science tools and frameworks. 


• Ability to support data exploration and data analysis tasks in support of analytics objectives. 


• Knowledge in model evaluation, tuning and performance, operationalization, and scalability of scientific techniques. 


• Proficiency in statistical modeling applications. 


• Proficiency in advanced SQL in multiple syntaxes.