Lead Data Scientist - AI & Machine Learning - No C2C

This role is open ONLY to US Citizens as all candidates must be eligible for secret security clearance (clearance requires US Citizenship). Please note, clearance isn’t required to start (only eligibility). Candidates who are Permanent Residents (Green Card Holders), TN visa holders, H1B or F1 (OPT/CPT) candidates are NOT permitted in this role. This role is NOT open to corp2corp (C2C) or corp2hire (C2H).

Lead Data Scientist – AI & Machine Learning

Work Arrangement: Hybrid – 3 days onsite, 2 days remote

Compensation: $140,000–$160,000 annually 10% bonus opportunity

Anticipated Start Date: January 4, 2027

Travel: Less than 10%

Relocation Assistance: Available for qualified candidates

Position Summary

We are seeking a highly skilled Lead Data Scientist to drive the development, implementation, and deployment of advanced analytics, machine learning, and artificial intelligence solutions across the enterprise.

This position operates at the intersection of business strategy and technical execution, partnering directly with business stakeholders to identify opportunities, translate operational challenges into data-driven solutions, and deliver measurable business value.

The ideal candidate combines strong technical expertise in AI, machine learning, and data science with business acumen, stakeholder engagement, and hands-on solution delivery. This is an individual contributor role requiring ownership of projects from initial business requirements and solution architecture through development, production deployment, and ongoing optimization.

Key Responsibilities

AI/ML Development & Technical Delivery

• Design, develop, test, and deploy production-ready machine learning models, predictive analytics solutions, and artificial intelligence applications.
• Own the end-to-end solution lifecycle, including business requirements gathering, data exploration, model development, validation, deployment, and performance monitoring.
• Develop and implement Generative AI and Large Language Model (LLM) solutions, including retrieval-augmented generation (RAG), intelligent agents, and model fine-tuning.
• Build supervised and unsupervised learning pipelines, statistical models, and advanced analytics frameworks to address complex business challenges.
• Collaborate with Data Engineering and DevOps teams to implement scalable MLOps practices, including CI/CD pipelines, model monitoring, data drift detection, and automated retraining.
• Apply coding, testing, documentation, and model evaluation best practices to ensure solutions are reliable, scalable, and maintainable.
• Evaluate emerging AI/ML technologies, frameworks, and methodologies to identify opportunities for innovation and improved business performance.

Business Partnership & Strategic Execution

• Partner directly with business leaders, operations teams, product managers, and other stakeholders to understand challenges and identify opportunities for AI and advanced analytics.
• Translate business requirements into actionable technical solutions with clearly defined deliverables and measurable outcomes.
• Support the development and prioritization of AI and analytics initiatives aligned with organizational objectives.
• Communicate technical concepts, analytical findings, and recommendations to both technical and non-technical audiences.
• Demonstrate the business value of implemented solutions through measurable improvements in efficiency, productivity, decision-making, or operational performance.
• Balance technical sophistication with practical business needs, emphasizing solutions that are sustainable, interpretable, and deliver tangible results.

Solution Architecture & Operational Excellence

• Design scalable AI/ML architectures that integrate with enterprise systems, data platforms, and existing business workflows.
• Collaborate with cross-functional technical teams to support cloud-based infrastructure, data pipelines, and production environments.
• Establish and maintain appropriate standards for model performance, documentation, testing, security, and governance.
• Incorporate responsible AI principles, including model explainability, data integrity, security, and algorithmic fairness.
• Troubleshoot technical challenges and continuously optimize deployed models and applications.
• Share technical knowledge and best practices with colleagues while contributing to a collaborative, delivery-focused environment.

Required Qualifications

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Operations Research, or a related quantitative field, or equivalent practical experience.
• 5 years of professional experience in data science, machine learning, advanced analytics, or related technical disciplines.
• Demonstrated experience independently developing and deploying AI/ML solutions into production environments.
• Strong hands-on proficiency with Python and commonly used data science libraries such as Pandas, NumPy, Scikit-Learn, PyTorch, or TensorFlow.
• Advanced SQL skills and experience working with complex datasets.
• Experience developing predictive models, machine learning pipelines, and AI-driven applications.
• Working knowledge of Generative AI, Large Language Models, and modern AI application architectures.
• This role is open ONLY to US Citizens as all candidates must be eligible for secret security clearance (clearance requires US Citizenship). Please note, clearance isn’t required to start (only eligibility). Candidates who are Permanent Residents (Green Card Holders), TN visa holders, H1B or F1 (OPT/CPT) candidates are NOT permitted in this role. This role is NOT open to corp2corp (C2C) or corp2hire (C2H).

Preferred Qualifications

• Master's degree or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Experience developing LLM-based applications, RAG architectures, intelligent agents, or fine-tuned models.
• Experience with enterprise data platforms such as Databricks, Snowflake, or Apache Spark.
• Familiarity with MLOps tools and frameworks such as MLflow, Docker, Kubernetes, Kubeflow, or Airflow.
• Experience with business intelligence and visualization tools such as Power BI or Streamlit.
• Experience implementing automated model monitoring, feature stores, or continuous model improvement processes.
• Background in business analysis, business operations, consulting, or another business-facing discipline prior to transitioning into data science or technology.
• Experience delivering AI or analytics solutions in a complex enterprise environment.
• Demonstrated ability to connect technical solutions to measurable financial or operational outcomes.

EOE (including Disability/Veterans)