Contractor AI/ML Data Scientist

Key Responsibilities:

• Data Analysis & Model Development: Conduct sophisticated data mining, predictive modeling, and statistical analyses to uncover insights from large-scale structured and unstructured datasets.
• Machine Learning Solutions: Design and develop machine learning models for real-world applications like recommendation systems, network dynamics analysis, and system performance prediction.
• MLOps & Automation: Partner with data engineers to refactor workflows and implement highly automated MLOps-based processes for streamlined model deployment and monitoring.
• Graph & Network Modeling: Build models to understand complex network behaviors, supply chain dynamics, and emergent patterns in large datasets. Develop metrics and models to describe and predict network performance.
• Performance Optimization: Optimize the performance and scalability of machine learning models, applying techniques such as numerical optimization, simulations, and model fine-tuning.
• Collaboration & Communication: Work cross-functionally with data engineers, product managers, and other stakeholders to ensure seamless integration of models into business applications. Communicate technical results to both technical and non-technical audiences.
• Research & Innovation: Stay at the forefront of AI/ML advancements, exploring new techniques in areas such as physics-informed neural networks (INNs), large language models (LLMs), and generative models to enhance our AI capabilities.

Required Skills & Qualifications:

• Education: BS, MS, or PhD in Computer Science, Data Science, Physics, Applied Mathematics, or related fields.
• Machine Learning Expertise: Strong experience in machine learning frameworks such as TensorFlow, PyTorch, or other relevant platforms. Proven expertise in developing and deploying machine learning models at scale.
• Data Science Tools: Proficiency in Python, NumPy, SQL, and data visualization tools such as Tableau. Familiarity with cloud platforms (AWS, Azure, Databricks) and open-source technologies like Kafka, Beam, and Neo4j.
• MLOps & CI/CD: Hands-on experience with MLOps pipelines, CI/CD tools (GitLab, Argo, Harness), and containerization (Docker, Kubernetes).
• Network & Graph Analysis: Strong understanding of graph mining, network analysis, and techniques for modeling network dependencies and resilience metrics.
• Statistical Analysis & Optimization: In-depth knowledge of statistical techniques, simulations, and numerical optimization to improve the performance of models.
• Communication Skills: Ability to explain complex technical findings to diverse audiences, ranging from data engineers to non-technical stakeholders.

Nice to Have:

• Experience with LLM models, fine-tuning, RLHF, and generative models.
• Expertise in observability and monitoring tools like Grafana, LOKI, and EFK.
• Previous experience with supply chain analytics or network analysis in industries such as e-commerce, banking, or insurance.