Job ID: 65041

Data Scientist

Client: City of Atlanta- Aviation

Duration

Location: 6000 N. Terminal Pkwy, Atlanta, GA 30320

Onsite

General Description And Classification Standards

The Data Scientist is responsible for maintaining and enhancing the organization's data infrastructure, creating advanced analytics solutions, and leveraging machine learning to improve decision-making and business efficiency. You will oversee the deployment, configuration, and continuous improvement of data models and infrastructure, ensuring robust performance and data security. In this role, you will work collaboratively with cross-functional teams to achieve organizational goals, offering innovative data-driven insights and solutions.

Supervision Received

Works under minimal supervision. May work independently with responsibility for specific functions or programs. Expected to take a lead role in guiding data strategy and mentoring junior data scientists or analysts.

Essential Duties & Responsibilities

Fully support, configure, maintain, and upgrade networks and servers at the Department of Aviation
Model Development and Optimization: Design, build, and refine predictive models and algorithms to enhance decision-making and drive business value.
Data Infrastructure Management: Maintain and optimize data pipelines, ensuring data integrity, security, and performance across the organization.
Data Strategy & Research: Research and propose cutting-edge data science techniques and methodologies to improve existing processes and introduce new data-driven initiatives.
Advanced Analytics & Insights: Provide deep analytical insights to solve complex business problems using statistical models, machine learning, and artificial intelligence.
Collaboration: Work with stakeholders across various departments (e.g., marketing, finance, operations) to understand data needs and deliver tailored analytics solutions.
Network & System Optimization: Ensure the organization’s data systems and models are operating at optimal levels, performing regular updates and improvements.
Performance Monitoring: Oversee the monitoring of data model performance, ensuring accuracy, scalability, and stability.
Technical Documentation: Write and maintain comprehensive documentation for all data science projects, including system architecture, model performance, and experiment results.
Customer-Focused Approach: Deliver clear, actionable insights to non-technical stakeholders, supporting business units in leveraging data for enhanced decision-making.
Mentorship: Act as a technical resource and mentor for junior data scientists and data analysts, guiding them in best practices, troubleshooting, and project execution.

Support

Essential Duties & Responsibilities

Respond to and resolve complex data-related issues, collaborating with IT support teams as necessary.
Ensure compliance with data governance and security standards.
Provide recommendations for improving data infrastructure and processes to increase business efficiency.
Deliver ongoing support for mission-critical business functions through data-driven strategies.
Develop and maintain data recovery and contingency plans in case of system failure or other disruptions.

Decision Making

Selects from multiple procedures and methods to accomplish tasks. Follows standardized procedures and written instructions to accomplish assigned tasks.
Selects appropriate data science methodologies and tools to achieve business goals.
Exercises judgment in balancing short-term project deliverables with long-term data strategy.
Influences business decisions by providing actionable insights based on thorough data analysis.

Leadership Provided

Serves as a technical resource or mentor to other employees. May lead or instruct less experienced workers in high level or technical jobs.
Acts as a technical leader in the data science team, providing guidance and mentorship to less experienced team members.
Contributes to the strategic direction of the data science function within the organization.
Presents insights and findings to upper management, recommending improvements to data strategy and network infrastructure.

Knowledge, Skills & Abilities

Technical Expertise: Deep knowledge of machine learning, artificial intelligence, data mining, and predictive analytics. Proficient in Python, R, SQL, and common machine learning frameworks (e.g., TensorFlow, Scikit-learn).
Data Infrastructure: Strong experience working with big data tools (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure, Google Cloud).
Problem-Solving: Ability to design and implement innovative solutions to complex problems, leveraging data to drive business improvements.
Communication: Excellent oral and written communication skills with the ability to explain complex technical concepts to non-technical audiences.
Collaboration: Proven ability to work across departments and manage multiple projects or tasks concurrently.
Leadership: Experience leading and mentoring teams, guiding them toward achieving technical excellence.
Security and Compliance: Familiarity with data security best practices, ensuring compliance with industry standards.

Non-Technical Skills

Project Management: Capable of managing multiple projects simultaneously while meeting deadlines and maintaining high standards of quality.
Self-Motivation: Uses initiative and independent judgment to undertake activities with minimal supervision.
Adaptability: Responds constructively to new information, changing conditions, and unexpected challenges.
Customer Service: Focuses on delivering value-driven, responsive solutions to both internal and external stakeholders.

Qualifications

Minimum Qualifications

Education And Experience

Education: Bachelor’s degree in Data Science, Computer Science, Mathematics, or a related field equivalent professional experience may be considered.
Experience: 5+ years of experience in data science, data engineering, or related fields with hands-on experience in deploying machine learning models.

Preferred Education & Experience

Master’s or PhD in Data Science, Machine Learning, Computer Science, or a related field.
Experience in leading data science teams or large-scale projects within an enterprise setting.
Experience with Databricks, including designing, deploying, and optimizing machine learning models in the Databricks environment.
Expertise in cloud platforms, especially Azure and AWS, including experience with data storage, model deployment, and scaling cloud-based solutions.
Experience with Environmental Systems Research Institute (ESRI) and geographic information system (GIS) analytics, with a focus on spatial data analysis and location-based insights.
Strong background in Azure Data Services (e.g., Azure Data Lake, Azure Machine Learning) and AWS Machine Learning Services (e.g., SageMaker, Redshift, Lambda)..

Certifications

Relevant certifications in machine learning, data science, cloud computing, or network administration (e.g., AWS Certified Data Analytics, DataBricks, Microsoft Azure Data Scientist Associate) are highly desirable

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