Data Science Manager
7 days ago
Lead and mentor a team of data scientists and engineers, fostering a collaborative and innovative work environment.Project Management:
Oversee the planning, execution, and delivery of projects related to speech analysis, voice technologies, and advanced models.Technical Expertise:
Provide technical guidance and hands-on support in the development and implementation of STT, TTS, and other voice-related technologies.Model Development:
Drive the development and optimization of advanced models like LLaMA3, Whisper, and Mistral for various applications.Data Management:
Ensure the integrity, quality, and security of data used in speech and voice technology projects.Collaboration:
Work closely with cross-functional teams, including product management, software engineering, and business stakeholders, to align project goals with business objectives.Innovation:
Stay updated with the latest advancements in speech analysis, voice technologies, and machine learning models, and integrate them into the team’s work.Performance Monitoring:
Establish metrics and KPIs to monitor the performance and impact of speech and voice technology solutions.Qualifications:Education:
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.Experience:
Minimum of 5 years of experience in data science, with a focus on speech analysis and voice technologies.Technical Skills:
Proficiency in STT, TTS, lead mining, and experience with models like LLaMA3, Whisper, and Mistral.Leadership:
Proven experience in leading and managing a team of data scientists and engineers.Programming:
Strong programming skills in languages such as Python, R, and familiarity with machine learning frameworks.Analytical Skills:
Excellent problem-solving and analytical skills, with the ability to interpret complex data and provide actionable insights.Communication:
Strong communication and interpersonal skills, with the ability to convey technical concepts to non-technical stakeholders.Preferred Skills:Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) for deploying and scaling machine learning models.Knowledge of natural language processing (NLP) and related technologies.Familiarity with data visualization tools and techniques.
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