Sr. Data Scientist

About the Opportunity

1. Job Summary

This is a full-time, one-year term appointment with the possibility of renewal.

The Senior Data Scientist at the AI Solutions Hub (AISH), the delivery arm of Northeastern University's Experiential AI Institute, will lead the development and delivery of AI solutions across diverse industries. The role involves building end-to-end AI pipelines—from business problem scoping to deployment and monitoring of production-grade models—with a focus on both Generative AI and Deep Learning.

The ideal candidate holds a Ph.D. in Deep Learning or Generative AI and brings a strong combination of academic and industry experience. They will possess deep, hands-on expertise in modern AI architectures including convolutional neural networks, transformers, and diffusion models. The role also requires significant experience in classical machine learning methods such as decision trees, gradient boosting machines, and both shallow and deep learning networks. A demonstrated ability to interface with clients to gather requirements, communicate insights, and lead solution design are essential. A successful candidate will also demonstrate mentoring experience and a strong track record of translating complex business needs into scalable AI solutions. Experience in the consulting industry is preferred.

2. Education & Experience

• Ph.D. (strongly preferred) or Master’s degree in Computer Science, Engineering, Applied Mathematics, Statistics, or a closely related field, with a focus on Deep Learning or Generative AI.

• Minimum of 5 years of industry experience in designing, developing, and deploying AI/ML solutions across sectors, including hands-on model building.

• Demonstrated academic and industrial contributions in Generative AI and Deep Learning, including practical deployment of models using modern architectures such as convolutional neural networks, transformers, and diffusion models.

• Proven experience building AI solutions using classical ML algorithms such as decision trees, gradient boosting machines, and shallow neural networks.

• Demonstrated client-facing experience, including engagement scoping, expectation management, and delivery leadership.

• Strong ability to translate complex technical findings into business insights for both technical and non-technical audiences.

• Track record of cross-functional collaboration and stakeholder engagement.

• Experience in commercializing AI technologies, including data-driven tools and platforms.

3. Knowledge, Skills, and Abilities

Technical and Analytical Expertise

• Advanced understanding of statistical methods, regression, hypothesis testing, and experimental design.

• Deep expertise in predictive modeling, classical ML algorithms (e.g., decision trees, gradient boosting), large language models (LLMs), generative AI, MLOps, and AutoML using frameworks like PyTorch, TensorFlow, HuggingFace, and LangChain.

• Demonstrated experience with modern AI model architectures including convolutional neural networks, transformers, and diffusion models.

• Demonstrated experience deploying ML systems into production environments, with a focus on performance, robustness, and scalability.

• Domain expertise in NLP, computer vision, or speech processing.

• Proficient in Python for software and ML pipeline development.

• Experience with SQL, NoSQL, and cloud platforms (AWS, Azure, GCP).

• Familiar with distributed data systems (e.g., Apache Spark) and workflow orchestration tools (e.g., Airflow, Prefect).

• Solid background in software development, including Linux, Git, and OOP languages such as Python, Java, or C++.

Project and Delivery Management

• Strong grasp of Agile/Scrum development practices.

• Proven industry experience in requirements gathering and scoping solutions.

• The ability to convert high-level business problems into actionable project plans and deliverables.

Client and Stakeholder Engagement

• Excellent interpersonal and communication skills to work directly with clients.

• Proven ability to develop custom AI strategies aligned with client goals.

4. Preferred Experience

• Hands-on experience with distributed data processing (e.g., Apache Spark, Hadoop).

• Track record of building and scaling ML pipelines for both structured and unstructured data.

• Proven record of technical leadership in architecture and delivery of robust and scalable AI systems.

• At least 3 years of MLOps experience, including de