Data Scientist

, programmatic, strategic, and operational analysis.

• Explain analytics model behavior/results in the vernacular of business.

• Perform technical risk analysis and reliability assessments.

• Perform multiple forms of advanced analyses, sustainment, optimization, text analytics, machine learning, parametric and non-parametric statistical models and techniques

• Build, test, validate and demonstrate analytical models through various relevant error metrics and calibration techniques

• Provide recommendations for plans, programs, strategies, policies and budgets.

• Design algorithms that require a number of different models/methods to be used in an ensemble

• Deploy models into production

The Team

Artificial Intelligence & Data Engineering

In this age of disruption, organizations need to navigate the future with confidence, embracing decision making with clear, data-driven choices that deliver enterprise value in a dynamic business environment.

The Artificial Intelligence & Data Engineering team leverages the power of data, analytics, robotics, science and cognitive technologies to uncover hidden relationships from vast troves of data, generate insights, and inform decision-making. Together with the Strategy practice, our Strategy & Analytics portfolio helps clients transform their business by architecting organizational intelligence programs and differentiated strategies to win in their chosen markets.

Artificial Intelligence & Data Engineering will work with our clients to:

• Implement large-scale data ecosystems including data management, governance and the integration of structured and unstructured data to generate insights leveraging cloud-based platforms

• Leverage automation, cognitive and science-based techniques to manage data, predict scenarios and prescribe actions

• Drive operational efficiency by maintaining their data ecosystems, sourcing analytics expertise and providing As-a-Service offerings for continuous insights and improvements

Qualifications

Required

• 3+ years of experience to have great understanding on the ML lifecycle and concepts

• 3+ years of experience with the design and implementation (building, containerizing, and deploying end to end automated data and ML pipelines) of automated cloud solutions

• 3+ years of experience in developing algorithms using data science technologies to build analytical models

• 3+ years of data extraction/manipulation experience using scripts specific to AI/ML

• 3+ years of IT experience. Minimum 2 years of relevant experience delivering AI/ML projects

• 3+ years of modeling experience using a variety of regression and supervised and unsupervised learning techniques.

• 3+ years of experience in data wrangling/cleansing, statistical modeling, and programming

• 3+ years of extensive experience working in an Agile development environment

• 3+ years of experience for fluency in both structured and unstructured data (SQL, NOSQL)

• 3+ years of production experience with Apache Spark

• 3+ years of hands-on experience with web APIs, CI/CD for ML, and Serverless Deployment

• 2+ years of experience with presentation and data analysis software such as: SAS, R, SPSS, MATLAB, QlikView, Excel and Access

• 1+ years of experience to have familiarity with Linux OS and Windows servers

• 1+ years of experience to have knowledge of Docker, Jenkins, Kubernetes, and other DevOps tools

• o Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future

• Must be a US Citizen

• Must live near, or relocate to, the Lake Mary, FL; Gilbert, AZ; or Mechanicsburg, PA areas

• Must be in your designated office location 10%-30% throughout the year

• Ability to travel up to 10%, on average, based on the work you do and the clients and industries/sectors you serve

• Bachelor's degree, preferably in Computer Sciences, Information Technology, Computer Engineering, or related IT discipline; or equivalent experience

Preferred

• Certification from any of the three major cloud platforms (AWS / Azure / GCP) in Cloud Architecture / Engineering / DevOps / ML.
• Familiarity with Kubeflow or MLflow
• Experience with machine learning pipelines (Azure ML)
• Familiarity with the latest Natural Language Processing or Computer Vision related algorithms