AI/ML Data Scientist

Job Title: AI/ML Data Scientist

Location: Remote

Job Overview:

We are seeking a highly skilled AI/ML Data Scientist to join our team and contribute to innovative solutions across diverse business and technical domains. In this role, you will develop, deploy, and optimize machine learning models, working with large-scale datasets and advanced analytics to drive business insights. You will be part of a dynamic team, collaborating with product managers, engineers, and customers to tackle complex challenges in fields like supply chain optimization, physical system design, and network analysis.

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.

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