We are looking for a Data Scientist Developer to explore and expand on the work of our team and who can utilize their skills to analyze and build our AI solution for our services at Magnify Access.

As a Data Scientist with proven AI/ML experience, you will play a pivotal role in integrating ML solutions into our platform to create a more accessible and inclusive workplace for employees with disabilities. You will suggest new and novel approaches to develop solutions. You thrive on building and fine-tuning Natural Language Processing (NLP) models, delving deep into data analysis, visualizing findings, and effectively communicating insights to the cross-functional team.

We want to hear from you if you want to expand your Data Science portfolio! Along with your resume, please submit a portfolio of previous work and/or provide links to showcase your best work on why you’d be perfect for this role.

The Data Scientist Developer will:

• Utilize advanced statistical techniques and machine learning algorithms to analyze large datasets from electronic health reports.

• Develop predictive models to extract recommendations from medical documents.

• Continuously assess and validate the performance of predictive models.

• Demonstrated expertise in data cleansing and preprocessing to ensure quality and consistency for analysis.

• Collaborate with the development team to deploy production models, for example, through REST API and MLOps tools.

You should have:

• Experience working with cloud-based data platforms, such as AWS.

• Strong communication (verbal and written) and collaboration skills.

The outcome of this internship will support the development of our dataset and language model. The deliverable will be a clear set of changes to these two components.

Regular virtual meetings with experienced staff for mentorship and guidance.

Comprehensive datasets.

Collaborative platforms to share and receive feedback on their work.

Opportunities to participate in team meetings and brainstorming sessions for a holistic understanding.

Training sessions on specific tools or strategies as required.

Feedback loops for continuous improvement and learning.

Exposure to real-world LM scenarios and challenges to enhance learning experiences.