Statistics, Biostatistics - AI trainer

Statistics, Biostatistics - AI trainer

Job Summary

Join our customer's team as a Statistics, Biostatistics - AI Trainer, playing a pivotal role in shaping advanced AI solutions through your expertise in data-driven methodologies. In this impactful remote, part-time position, you will guide AI training initiatives that rely on robust statistical and biostatistical frameworks, directly influencing the next generation of intelligent systems.

Key Responsibilities

1. Develop, curate, and deliver high-quality training materials in statistics and biostatistics for AI development projects.
2. Assess AI models and datasets, ensuring rigorous statistical validity and methodological soundness.
3. Collaborate with multidisciplinary teams to identify project needs and tailor training content accordingly.
4. Coach and mentor team members and AI models on the application of advanced statistical concepts.
5. Facilitate interactive training sessions and workshops, fostering a culture of continuous learning and improvement.
6. Provide detailed feedback and documentation to support ongoing refinement and optimization of AI training processes.
7. Stay updated on emerging trends in statistics, biostatistics, and their intersection with artificial intelligence.

Required Skills and Qualifications

1. PhD or Master’s degree in Statistics, Biostatistics, or a closely related field.
2. Demonstrated experience applying statistical methodologies in academic, research, or industry AI projects.
3. Exceptional written and verbal communication skills, with a keen ability to convey complex concepts clearly.
4. Hands-on experience with statistical software (such as R, SAS, or Python) and data analysis frameworks.
5. Proven ability to create engaging instructional content for technical audiences.
6. Strong analytical thinking and problem-solving abilities.
7. Self-motivated, organized, and adept at working independently in a remote environment.

Preferred Qualifications

1. Experience in AI model training, machine learning, or related data science initiatives.
2. Background in educational or instructional design within scientific or technical domains.
3. Publications or notable contributions in the fields of statistics or biostatistics.