Sr. Data Scientist

• At least six years of hands-on experience developing, deploying, and maintaining AI/ML products within a large, professional, or academic organizations.
• Bachelor's degree with specialization in a technology related field (Computer Science preferred, Master's preferred) from an accredited college or university, or equivalent combination of directly related education and/or experience.
• Advanced proficiency in coding and experience in python and several other programming languages.
• Advanced knowledge and understanding in statistical modeling and data analysis techniques, platforms, and software.
• Advanced analytical and problem-solving skills.
• Advanced knowledge of AI/ML, NLP and Generative AI technologies, frameworks, and solutions.
• Advanced knowledge of cloud data platforms, technologies, frameworks, and solutions.
• Demonstrated ability to work collaboratively with cross- functional teams and communicate effectively with both technical and non-technical audiences.
• Hands-on experience design, developing and deploying AI/ML products into a cloud environment.
• Proficiency of existing and emerging trends within the AWS cloud environment
• Expert proficiency in Python data science development.
• Advanced experience with Natural Language Processing (NLP) techniques like Text Normalization, Named Entity Recognition, and Part-of-Speech (POS). Familiarity with more advanced techniques such as word embeddings. Specific experience popular AI/ML frameworks such as Transformer, Scikit-learn, SpaCy, XGBoost.

Development of New Machine Learning and AI Features/Products:

• Collaborate with teams to understand their unique situations, challenges and opportunities. Utilize the knowledge gained to support the curation of potential new opportunities, leveraging AI/ML and Generative AI technologies.
• Take a hands-on, iterative approach in researching, designing, and building new machine learning and AI features/products.
• Identify the most valuable models and methodologies, implement and train the models, and contribute to their deployment into the production environment.

Maintenance and improvement of operational machine learning (ML) and artificial intelligence implementations:

• Establish a detailed technical understanding of existing products that leverage AI/ML and Generative AI technologies - including the training datasets, model training methodologies, technical infrastructure, and model monitoring approach.
• Work collaboratively with product owner and architect to maintain a detailed roadmap for AI/ML epics, features, and stories - organizing the tangible work needed to maintain and improve upon our products.
• Monitor accuracy and precision of models over time.
• Lead efforts to retrain models by expanding upon underlying training datasets, introducing new features, and working with others to ensure training data is of highest quality.