Job Type
Full-time
Description
NOTE: This opportunity is full-time employment position only (no 1099 or C2C engagements, or third parties or staffing agencies, please). The candidate
MUST be a U.S. Citizen or Permanent Resident (Green Card holder). This is a remote opportunity; candidate must be based in the U.S.; have resided in the U.S. for at least 3 years in the past 5 years; ET time zone work schedule.
Daily Responsibilities- Analyzes unstructured and semi-structured data, applying creativity to large-scale analysis for high-value use cases using advanced algorithms in distributed and cloud-based infrastructures. s. Utilizes advanced tools for interpreting complex data, delivering recommendations for business decisions. Experience in software development, data transport APIs, Cloud-based tools, and visual analytics, with expertise in open-source stacks, Windows development, and various data analysis technologies.
- Execute and advance the enterprise data science and AI strategy aligned to organizational goals, serving as a trusted advisor on advanced analytics, machine learning, and AI adoption.
- Lead high-impact AI/ML initiatives across business and technology teams, delivering proofs of concept and MVPs that mature into scalable production solutions.
- Translate complex business challenges into analytical frameworks and scalable AI-driven solutions that support strategic decision-making.
- Design, develop, and deploy advanced machine learning solutions, including predictive modeling, forecasting, NLP, large language models (LLMs), recommendation systems, optimization models, RAG, and other AI-powered applications.
- Apply advanced data science techniques including deep learning, ensemble methods, time series analysis, experimentation, A/B testing, and statistical modeling.
- Lead hands-on model development in Python, establishing best practices for reusable code, testing, reproducibility, feature engineering, and utilization of modern data science frameworks and libraries.
- Partner with AI and engineering teams to implement end-to-end MLOps practices, including model versioning, automated training and deployment pipelines, monitoring, drift detection, and continuous model improvement.
- Collaborate with data engineers and architects to build scalable data platforms, pipelines, and cloud-based solutions that support large-scale structured and unstructured data.
- Establish and enforce standards for model validation, explainability, interpretability, data quality, governance, responsible AI, bias mitigation, transparency, and auditability.
- Communicate complex analytical insights to executive and non-technical stakeholders through effective data storytelling, visualization, and strategic recommendations.
- Mentor and develop data science talent while leading ross-functional teams to deliver high-impact data science and AI solutions.
Requirements
- Must of a U.S. Citizen or Permanent Resident (Green Card holder), as mandated by our government client.
- Must be able to complete/pass/hold at a minimum a Public Trust Investigation / background check. An active Public Trust or higher is preferred.
- Must be based / reside in the U.S.
- 10+ years of experience in data science, machine learning, or applied AI with deep expertise in machine learning, deep learning, and LLM-based approaches.
- 6+ years of demonstrated experience leading enterprise-scale data science initiatives.
- Expert-level proficiency in Python for data science and machine learning, including hands-on Python experience delivering production-grade data science solutions.
- Proven experience building and deploying ML models in production environments.
- Strong experience with MLOps tools, pipelines, and lifecycle management.
- Experience with LLMs, NLP, or generative AI applications.
- Experience in AI governance, model risk management, or ethical AI.
- Proven experience implementing MLOps frameworks and production ML systems (e.g., MLflow, Kubeflow, Azure ML, or SageMaker).
- Experience with big data tools (e.g., Spark) and cloud platforms (AWS, Azure, GCP).
- Strong SQL skills for data extraction, transformation, and analysis.
- Proficiency with data visualization and BI tools (e.g., Power BI, Tableau).
- Familiarity with federal AI governance frameworks, including the NIST AI Risk Management Framework and OMB AI guidance.
- Experience working with sensitive data, including PII safeguards such as anonymization, masking, and data loss prevention.
- Experience with generative AI tooling, including RAG frameworks, embedding models, and vector databases.
- Strong foundation in statistics, experimentation design, and model evaluation (including precision, recall, F1 score, and related performance metrics.
- Ability to process high-volume data collections and streams, making discoveries in the realm of big data.
- Requires strong technical and computational skills for coding, designing, and deploying sophisticated applications in unstructured data analysis.
- Excellent analytical skills, attention to detail, and strong problem-solving abilities.
- Excellent communication and collaboration skills to communicate complex analytical insights to executive and non-technical stakeholders. Ability to translate ambiguous business questions into analytical solutions.
- BS or MS degree (preferred) in data science, computer science, statistics or related field.
Compensation decisions depend on a wide range of factors, including but not limited to skill sets, experience and training, security clearances, licensure and certifications, and other business and organizational needs. $145k -$175k.
Salary Description
$145k - $175k
