Data Engineer/Scientist (Cloud and On-Prem)

The ideal candidate should have:

• More than 7 years of experience in the Information Technology industry, managing client expectations
• Knowledge of healthcare jargon and terminology is a big plus
• A deep understanding of the challenges and opportunities associated with managing data in a cloud environment, data security
• A proven track record of developing and implementing data governance frameworks ensuring that data is managed in a consistent and compliant manner
• A proven track record of success in implementing solutions that are scalable, secure, and compliant with federal standards and regulations
• Possess the combination of knowledge, skills and abilities to assist the U.S. Government agency in achieving the task order goals and in meeting its platform requirements
• Strong expertise in preparing data for analytical or operational uses, building data pipelines to bring together information from different healthcare source systems and data sources
• Advanced skills in developing intelligent AI algorithms and deploying them into applications. Building scalable, efficient APIs to integrate data products and sources into applications. Creating Infrastructure as Code to ensure reproducibility and scalability of AI solutions.
• Advanced analytics skills in machine learning and predictive modeling.
• Strong Tableau visualization skills.
• Deep understanding of advanced data analysis using statistical modeling techniques to extract insights and identify trends from complex datasets
• A proven track record of developing and optimizing scripts using Python and SQL to manipulate, transform, and analyze large-scale datasets
• Proficient in different AI /ML algorithms, such as clustering, forecasting, predictions, anomaly detection, deep learning, recommendation systems, reinforcement learning, neural networks, regression algorithms languages, and frameworks, image processing, vision, optical character recognition:
• Proficient in Machine learning and deep learning algorithms/skills: The ideal candidate should have a strong background with a proven track record in deep learning, machine learning, and programming the system to learn from data and make accurate predictions. Expertise in developing tools that can identify patients at risk for certain events, predict the effectiveness of treatments, and improve the accuracy of diagnoses.
• Natural language processing: the candidate shall have a strong knowledge and skills to develop tools that can extract information from medical records, summarize clinical trials, and generate patient education materials
• Computer vision: deep knowledge of computer vision to develop tools that can detect abnormalities in medical images, such as X-rays and MRIs
• Experience in building Advanced Analytics - prototype development
• Implementation of machine learning solutions end-to-end from hypothesis to backend and frontend development. Proficiency in programming languages such as Python, R
• Strong coding ability in producing clean and effusion code as well as debugging and understanding large code bases
• Experience with big data tools and frameworks (like Hadoop, Spark)
• Deep understanding of databases and data modeling /design techniques and data interface protocols

Data Security

• Comfortable with addressing organization's data security, cybersecurity architecture, and systems security engineering requirements throughout the acquisition and product life cycle
• Ability to contribute to creation and execution of enterprise security vision
• Experience in Cloud Security, Threat and Vulnerability Management (TVM), Security Configuration Checklists
• Extensive knowledge of health information and health care services regulatory environment including HIPPA and medicaid/medicare.
• Knowledge in HIPPA implementation
• Understanding of NIST SP 800-53 controls
• Understanding the Supply Chain Risk Management (SCRM) procedures - implement and deploy best practices and plug security gaps within its cyber supply chain
• Experience in risk identification and assessment, determination of appropriate risk response actions, development SCRM plans to document response actions, and monitoring performance against plans
• Experience in enhancing SCRM Plan to align with National Institute of Standards and Technology (NIST) Special Publication (SP) 800-161, Supply Chain Risk Management Practices for Federal Information Systems and Organizations
• Design, creation, & implementation of information security vulnerability management policies, procedures, and standards
• Knowledge and experience with defense strategies, disaster recovery, and fault- tolerant cloud security infrastructure in compliance with cybersecurity frameworks (NIST, CIS, ISO/IEC 27001 and 27002)
• Deep understanding of security configurations, updates, action plans, compliance audit report, risk management program, adherence to security plans, and historical trending
• Ability to evaluate new security technologies and emerging threats to provide recommendations to strengthen the cloud environment

Data Documentation

• Document context of data collection
• Document data collection methodology
• Document the current and future data flow architecture between existing systems including all interfaces and control checks
• Document structure and organization of data files
• Document sata manipulations through data analysis from raw data
• Document data confidentiality, access and use conditions
• Document data models and schemas (data elements,flowcharts that illustrate data entities, their attributes, types, formats, values, domains, keys, relationships, constraints, and rules that define the data structure and meaning)

In addition, the candidate should have:

• US Citizenship or be a Green Card holder
• Strong and clear communication skills, writing skills
• Ability to clearly articulate your thoughts and ideas
• Strong leadership skills and experience in managing complex projects
• Knowledge of data management, data governance, and analytics projects
• Understanding of agile methodologies and risk management
• Skills in managing the human side of change as organizations evolve their data capabilities
• Experience with training, communication, and overcoming resistance to change
• Understanding of the organization's culture and the ability to foster positive change
• Experience with statistical analysis and data interpretation
• Knowledge of data collection methods, data analytics, and visualization tools
• Ability to translate complex data into clear, actionable insights
• Deep understanding of algorithms, statistical analysis, and data mining techniques
• Expertise in data governance, data quality, and data management best practices
• Experience with developing data strategies and roadmaps
• Understanding of how data intersects with business strategy and objectives
• Skills in designing, building, and maintaining data infrastructure
• Proficiency in SQL and data warehousing solutions
• Experience with data governance practices, data quality, and related data management discipline
• Good understanding of regulatory standards and data privacy laws
• Skills in metadata management and data classification
• Expertise in cloud computing, with deep knowledge of the major cloud service providers, like AWS, Google Cloud, or Microsoft Azure
• Experience in designing, implementing, and managing secure, scalable cloud architectures
• Skills in migrating legacy systems to the cloud, and in hybrid cloud environmentsProficiency in automation and orchestration tools, as well as in cloud networking and security
• Understanding of cost-effective system design and service selection in the cloud
• Extensive experience in user-centered system design
• Skills in influencing and collaborating with diverse teams (e.g., data analysts, engineers, strategists) to ensure the user perspective is integrated throughout the project

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Technical Qualifications:

Experience with performing data maturity assessments and applying data governance frameworks in an enterprise environment. Experience designing architecture solutions for a variety of data use cases both on premise and in the cloud to support high volume, velocity, variety, and veracity data using industry best practices and considering compliance with applicable federal and industry mandates and or initiatives. Understanding of advanced analytics techniques. Understanding of data modeling principles and best practices. Understanding of data integration best practices, and metadata management principles.

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