Microsoft Applied Sciences Manager 

Taiwan, Taoyuan City 

53344346

• Deep expertise of 10+ years in machine learning algorithms, model development, and deployment.
• Proven experience of 2+ years leading and mentoring a team of data scientists and machine learning engineers.
• Master's or Ph.D. in Computer Science, Machine Learning, or a related field.
• Proficiency in programming languages such as Python/Jupyter, TensorFlow, or PyTorch. - Experience with deploying machine learning models in production environments.
• Strong understanding of software engineering principles and best practices.
• Excellent problem-solving and analytical skills.
• Effective communication and collaboration skills.
• Additional experience in any of the areas like cyber security, fraud detection, regulations, business processes in enterprises, legal discovery, etc., would be beneficial, but not mandatory.

We are seeking an Applied Research Manager Responsibilities include:

Team Leadership:

• Lead and mentor a team of applied researchers, fostering a collaborative and innovative work environment.
• Provide technical guidance and support to team members, helping them grow in their careers and achieve their goals.
• Drive the team's strategic vision and align it with the overall company objectives.

Research and Development:

• Conduct cutting-edge research in Gen AI, autonomous agents, LLMs, data science, machine learning, and applied statistics to solve complex business problems.
• Collaborate with cross-functional teams to identify opportunities for applying advanced analytics and machine learning techniques.
• Stay abreast of the latest developments in the field and ensure the team remains at the forefront of technology.

Project Management:

• Oversee the end-to-end lifecycle of data science projects, from problem definition to model deployment.
• Ensure projects are delivered on time and within scope, meeting both technical and business requirements.
• Collaborate with stakeholders to define project goals, success criteria, and deliverables.

Technical Expertise:

• Provide technical leadership and contribute hands-on expertise in areas such as machine learning, statistical modeling, and data analysis.
• Review and guide the development of algorithms, models, and methodologies to solve complex problems.

Collaboration and Communication: -

• Collaborate with cross-functional teams, including engineering, product management, and business stakeholders, to drive data science initiatives.
• Communicate complex technical concepts and findings to non-technical stakeholders in a clear and understandable manner.