Microsoft Data Scientist II 

India, Telangana, Hyderabad 

871171504

Required Qualifications:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
  • OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical tec
  • OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical tec
  • 1+ experience in data science, machine learning, or a related field.
• OR equivalent experience.

Additional or Preferred Qualifications:

• Expertise in Python, C#, or another programming language for building scalable solutions.
• Experience Proficiency in machine learning frameworks like PyTorch, TensorFlow, or scikit-learn.
• Experience with algorithms for classification, clustering, and anomaly detection.
• Familiarity with techniques for handling noisy or imbalanced datasets.

• Machine Learning Development: Utilize large language models (LLMs), traditional algorithms, and machine learning techniques to automatically understand products and generate and execute test scenarios for various Microsoft products.
• Bug Feature Detection: Develop and implement solutions to automatically detect features of logged bugs, enabling efficient deduplication, assignment, and prioritization.
• Data Management: Handle the collection, cleaning, and labelling of data, ensuring high-quality datasets for training and optimizing machine learning models.
• Performance Measurement: Apply various performance measurement techniques to evaluate and improve the effectiveness of machine learning models.
• Collaboration: Work closely with cross-functional teams, including product management, Leadership, and partner dev teams, to ensure the successful implementation of AI-driven solutions.
• Productionise: Hands on design/implementation of productionizing ML / LLM solutions end to end with proper testing.
• High Quality Coding: Ensure high quality code is checked in and is peer reviewed for getting continuous feedback. Prompts, ML training, Exploratory Data Analysis (EDA), data cleaning and labelling code are all equally important code components and should be maintained in same high standards.