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About This Role
Overview:
Geosyntec has an exciting opportunity for a Professional Software/Data Scientist/Engineer with an emphasis on Artificial Intelligence (AI) in our Kennesaw, GA office. This role leverages strong foundations in computer science and software engineering to design, build, and deliver data\-intensive and AI\-enabled systems that accelerate internal innovation and client\-facing digital solutions. The role blends modern software engineering, data engineering, and applied AI practices. You will partner with our engineering, research and development teams, provide best\-in\-class solutions, and ensure our digital tools remain cutting\-edge. Candidates may come from software engineering, data science/engineering, or hybrid technical backgrounds, provided they have experience delivering production\-grade systems in data\-intensive or AI\-enabled environments. The candidate will support our ongoing operations through a focus on frontend, backend software development, database management and other tools to compile, visualize and analyze data from a variety of streaming and static sources; build interactive dashboards and reports; and assist with the design, development, and implementation of new products and services for our clients.
The position leads the design and delivery of enterprise\-grade AI and digital solutions supporting Geosyntec’s highest\-priority initiatives. The role owns end\-to\-end solution architecture spanning data platforms, software services, AI and generative AI systems, and application integration. Solutions are designed to be secure, scalable, resilient, cost\-efficient, and aligned with enterprise governance, cybersecurity, and IT standards. The position works with AI/digital and other leaders and translates business requirements into actionable AI solution architectures spanning data platforms, model selection and deployment, application integration, orchestration frameworks, and AI extensibility layers.
The role partners cross\-functionally with product, engineering, security, data, and infrastructure teams to drive adoption of AI capabilities and establish best practices across the enterprise.
Geosyntec is an innovative, international engineering and consulting firm serving private and public\-sector clients to address new ventures and complex problems involving our environment, natural resources, and civil infrastructure. Our engineers, scientists, technical and project employees serve our clients from offices across the world. Ranked by ENR as one of the top environmental engineering design firms, Geosyntec is internationally known for its technical leadership, broad experience, and exceptional client service.
We invest in our people. Each employee is unique, and your career at Geosyntec will be too. We offer competitive pay and benefits, and well\-being programs to support you and your family.
To Learn More Visit: http://www.geosyntec.com/careers/.
Essential Duties and Responsibilities:
- Design and lead AI\-based/digital solutions and services, selecting optimal architectures aligned with the company’s cloud, data, and security standards.
- Translate business requirements into implementable architectures and solutions across data platforms, AI models, application services, orchestration frameworks, and extensibility layers.
- Design, develop, test, deploy, support and enhance new or off\-the\-shelf secure AI and ML models, including Retrieval\-Augmented Generation (RAG), tailored to domain\-specific deliverables and company initiatives. Build the Generative AI platforms, systems and infrastructure and apply general knowledge of software development and infrastructure as code practices
- Automate engineering and business processes using scripting languages and modern automation platforms, including Python and cloud\-based AI services from Microsoft (e.g., Copilot Studio, Azure Foundry, etc.).
- Design and implement user interfaces, data visualizations, and other interaction layers as needed to support AI\-enabled workflows and decision\-making.
- Develop Python scripts for data analysis and automation of engineering analysis.
- Design, develop, test, deploy, support, and enhance production\-grade applications and services that enable AI and data\-driven workflows.
- Integrate front\-end components with back\-end APIs and services for seamless data interactions.
- Ensure UI/UX best practices to improve user experience and workflow efficiency.
- Develop, manage, and maintain relational database applications for data storage and retrieval, ensuring performance, scalability, and security.
- Implement and maintain RESTful APIs and backend services to support frontend applications.
- Lead solution architecture for priority AI, generative AI, and agent\-based use cases, including hands\-on technical design, decision\-making, and architecture reviews. Identify technical, security, data, and delivery risks associated with AI solutions and implement appropriate mitigation strategies.
- Embed Responsible AI principles into solution designs, including privacy, security, transparency, and model risk management. Maintain a strong awareness of the latest trends in AI/ML and their applications to engineering consulting and related industries, ensuring that the company remains at the forefront of technological advancements.
- Provide training and support to internal teams on AI/ML technologies and best practices, fostering a culture of continuous learning and innovation.
- Maintain comprehensive documentation for all AI/ML projects, including design specifications, solution diagrams, implementation plans, and user guides.
- Responsible for coordinating transportation to and from the office; and local travel or driving to project sites as needed.
- Drive personal, company, and rental vehicles to client or company project or office sites, and other business locations, as needed
Education and Licensure:
- Bachelor’s degree in Computer Science, Information Systems, Data Science or related field. (required)
Skills, Experience and Qualifications:
- At least 5 years (7\+ preferred) of related work experience or equivalent combination of education and experience. (required)
- Demonstrated experience in software engineering, working with large data sets, and developing end\-to\-end workflows in a professional environment. (required)
- Proficiency in at least one modern programming language (for example, Python, C\#, Java, or similar) and ability to write maintainable, testable code. (required)
- Proven experience in developing and deploying AI/ML models, with a strong understanding of RAG models and their implementation using LLMs. (required)
- Proficiency with vector stores, semantic search, and related technologies. (required)
- Knowledge of fine\-tuning techniques for LLMs to adapt them to specific tasks and domains is required. (required)
- Familiarity with other AI and ML technologies such as reinforcement learning, computer vision, or recommendation systems. (preferred)
- Experience with or ability to learn and work with Microsoft Azure services, Microsoft Foundry, Azure OpenAI, Copilot Studio, Power Platform, cognitive search, vector databases, and API integrations. (required)
- Strong understanding of identity, networking, data governance, and security within cloud\-based environments. (required)
- Demonstrated ability to lead technical design discussions, manage stakeholders, and discuss with engineering teams. (required)
- Strong communication skills and ability to translate needs between business stakeholders and technical teams. (required)
- Valid U.S. driver’s license and a satisfactory driving record for business travel. (required)
- Health \& Safety training, medical monitoring, and client\-driven drug and background testing may be required by clients for specific project needs.
- This position is not eligible for visa/employment sponsorship.
Preferred Candidate Attributes:Problem Solving \& Analytical Thinking: Self\-learner and desire to take initiative. Applies structured thinking to identify, analyze, and help solve business and engineering problems using digital/AI\-enabled approaches. Delivers practical solutions with guidance from senior team members.Cross\-Functional Collaboration: Works effectively with engineers, data, and business stakeholders to support AI solutions. Demonstrates a collaborative mindset and contributes positively within cross\-disciplinary teams.Communication \& Technical Storytelling: Communicates ideas and technical concepts clearly, adjusting style and level of detail to the audience. Listens actively, seeks feedback, asks clarifying questions, and summarizes discussions to ensure shared understanding.Learning Agility \& Growth Mindset: Learns quickly through hands\-on experimentation and mentorship. Embraces new tools, technologies, and methods, applying lessons learned from both successes and setbacks to improve future work.Innovation \& Curiosity: Brings curiosity and fresh perspectives to problem\-solving. Contributes ideas, explores new approaches, and collaborates with others to refine and strengthen AI solutions that support business outcomes.
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Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Geosyntec Consultants, Inc., this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Geosyntec Consultants, Inc. AI Hiring
Geosyntec Consultants, Inc. has 1 open AI role right now. They're hiring across Data Scientist. Based in Kennesaw, GA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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