Lead Data Scientist

3 weeks ago

Herndon, United States Collabera Full time

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.

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