Responsibilities

You will work with teams of talented ML scientists and attract best scientists in ML.
# Represent MCDS to various personas across data scientists to the C-suite across Microsoft and externally
# Lead proof-of-value and quantify business impact
# Seek, give and receive critical feedback including but not limited to code review
# Ensure results and retention of the team conducting regular 1x1s, team meetings, providing feedback, coaching delegation
# Collaborate with business stakeholders to ensure ML solutions successfully deliver the business outcomes
# Excellent Program Management organizational abilities

The candidate must demonstrate the following:

# Strong expertise in Python programming and one of the Deep Learning frameworks (PyTorch, MXNet, TensorFlow, Keras)
# Knowledge of (with deep expertise in at-least three of) Classification, Prediction, Recommender Systems, Time Series Forecasting, Anomaly Detection, Optimization, Graph ML, NLP
# Hands-on develop ML models using the above techniques across Customer, Partner, Field Sales in varied domains
# Ability to both use existing libraries (ML, Deep Learning, Re-inforcement Learning) as well as design algorithms ground-up
# Able to prepare data pipelines and feature engineering pipelines to build robust models using SQL, PySpark, Azure Data Studio etc
# Strong research record preferred demonstrated, through publications
# Scientific thinking and ability to invent: Prior experience creating intellectual property through patents desirable

Required Qualification :

• Masters degree in Computer Science, Mathematics, Statistics, Physics or related field. Focus on ML preferred. Ph.D. desirable.
• 12+ Years of experience in ML, Deep Learning in industry/academia
• 6+ Years of experience recruiting and managing Ph.D. level scientist, researchers and engineers
• Strong expertise in Python programming and one of the Deep Learning frameworks (PyTorch, MXNet, TensorFlow, Keras)
• Knowledge of (with deep expertise in at-least three of) Classification, Prediction, Recommender Systems, Time Series Forecasting, Anomaly Detection, Optimization, Graph ML, NLP
• Hands-on develop ML models using the above techniques across Customer, Partner, Field Sales in varied domains
• Ability to both use existing libraries (ML, Deep Learning, Re-inforcement Learning) as well as design algorithms ground-up
• Able to prepare data pipelines and feature engineering pipelines to build robust models using SQL, PySpark, Azure Data Studio etc
• Strong research record preferred demonstrated, through publications
  Scientific thinking and ability to invent.

Good to have :

# Proficient in Relational Databases (SQL) and Big Data Technologies (Hive, PySpark)
# Exposure to real-world MLOps
# Knowledge of working in cloud-computing environment like Azure or AWS or Google Cloud. Azure preferred

#Prior experience creating intellectual property through patents