Data Scientist (Ml) Jobs

About ModuleQ
Headquartered in Cupertino, California, ModuleQ connects the dots in work data by tapping into the largest databases, the latest news, and internal content. With consent, ModuleQs People-Facing AI creates a network of intelligence in clients existing workspace to identify priorities. ModuleQ uses proprietary algorithms to search public and internal information as well as business intelligence from partners.
What you'll do

• Communicate findings, recommendations, and insights to technical and non-technical stakeholders.
• Cleanse and validate data, ensuring consistency and uniformity to optimize model performance.
• Develop and implement state-of-the-art algorithms and models that underpin AI features and capabilities.
• Analyze data to identify trends, patterns, and insights that drive informed decision-making.
• Build and curate datasets by labeling and annotating data for supervised and unsupervised learning.
• Collaborate on interdisciplinary projects, fostering a culture of cross-functional teamwork.
• Facilitate the seamless transition of AI solutions from development to production environments.
• Perform thorough data and error analysis to continuously refine and enhance AI models' performance.
• Conduct experimental analyses to derive meaningful insights, contributing to product innovation.
• Identify and source relevant structured and unstructured data from various internal and external sources.
• Collaborate closely with engineering and product teams to comprehend requirements and translate them into effective AI features.
• Collect, preprocess, and transform data into usable formats, ensuring accuracy, completeness, and quality.

What you bring

• Excellent communication skills
• Experience with training transformers, MLOps and tools such as SpaCy and Azure ML is a plus, as is experience with C#
• Master's Degree in Computer Science, Algorithms, or a relevant field
• The candidate should have experience with modern machine learning and data science toolkits such as PyTorch/Tensorflow, Pandas, NumPy, Scikit-learn, Jupyter/Python.