Data Scientist

NucleusTeq•Phoenix, AZ

30d•Remote

About The Position

The Data Scientist role is responsible for the development of NuoData, an AI-powered data management platform proprietary to NucleusTeq, Inc. This position involves leading cross-functional teams, collaborating with stakeholders, overseeing the development and deployment of AI/ML models, and ensuring data quality and governance. The role also includes performing exploratory data analysis, managing project roadmaps, and mentoring junior team members.

Requirements

Master’s Degree in Data Analytics
6 months of experience as a Data Scientist
Experience as a Deputy Manager is acceptable
Any suitable combination of education, training or experience thereof.

Responsibilities

Develop and manage project roadmaps for AI and Machine Learning-driven products, ensuring timely delivery of features that leverage large-scale data models and AI systems.
Lead cross-functional teams of data scientists, engineers, and designers to implement AI/ML models, ensuring seamless integration into production environments.
Collaborate with stakeholders to define AI/ML and data product requirements, ensuring alignment with business goals and project objectives for large-scale data solutions.
Oversee the development, testing, and deployment of AI and machine learning models, ensuring that models meet performance, accuracy, and scalability requirements.
Ensure effective data collection, cleaning, and transformation processes, applying best practices in data governance to ensure that AI and ML models are built on high-quality, structured data.
Continuously monitor and evaluate the performance of AI/ML models using appropriate metrics (e.g., accuracy, precision, recall, F1-score) and adjust models as needed based on feedback and performance reports.
Oversee the integration and deployment of LLMs (e.g., GPT, BERT) into products for NLP tasks such as sentiment analysis, chatbots, and customer service automation.
Implement Agile Scrum or Kanban methodologies for AI/ML projects, ensuring iterative and incremental development of machine learning models, and continuous delivery of AI-driven features.
Perform exploratory data analysis (EDA) on large datasets to identify trends, patterns, and insights that inform AI model development and data-driven decision-making.
Act as the primary point of contact for stakeholders, providing regular updates on the progress of AI/ML projects and translating complex technical data into business friendly insights.
Proactively identify and manage risks related to the deployment of AI/ML models, such as data quality issues, model performance degradation, and biases in AI algorithms.
Implement monitoring frameworks to evaluate the real-world performance of AI/ML models post-deployment, ensuring models adapt and evolve based on new data and user feedback.
Ensure AI/ML models comply with industry regulations and ethical guidelines, managing data privacy, bias mitigation, and transparency in model decision-making processes.
Use insights from data analysis and AI models to recommend product enhancements and optimizations, leading to improved user experience and business outcomes.
Provide mentorship to junior team members on AI/ML concepts, best practices for model development, and data engineering techniques.