Principal Data Scientist

As an expectation a fitting candidate must have/be:

• Ability to analyze business problem and cut through the data challenges.
• Ability to churn the raw corpus and develop a data/ML model to provide business analytics (not just EDA), machine learning based document processing and information retrieval
• Quick to develop the POCs and transform it to high scale production ready code.
• Experience in extracting data through complex unstructured documents using NLP based technologies.
  
  
  

Good to have: Document analysis using Image processing/computer vision and geometric deep learning

Technology Stack:

Python as a primary programming language.

Conceptual understanding of classic ML/DL Algorithms like Regression, Support Vectors, Decision tree, Clustering, Random Forest, CART, Ensemble, Neural Networks, CNN, RNN, LSTM etc.

• Programming:
• Must Have: Must be hands-on with data structures using List, tuple, dictionary, collections, iterators, Pandas, NumPy and Object-oriented programming
• Good to have: Design patterns/System design, cython
• ML libraries:
• Must Have: Scikit-learn, XGBoost, imblearn, SciPy, Gensim
• Good to have: matplotlib/plotly, Lime/sharp
• Data extraction and handling:
• Must Have: DASK/Modin, beautifulsoup/scrappy, Multiprocessing
• Good to have: Data Augmentation, Pyspark, Accelerate
• NLP/Text analytics:
• Must Have: Bag of words, text ranking algorithm, Word2vec, language model, entity recognition, CRF/HMM, topic modelling, Sequence to Sequence
• Good to have: Machine comprehension, translation, elastic search
  
  
  
  

• Deep learning:
• Must Have: TensorFlow/PyTorch, Neural nets, Sequential models, CNN, LSTM/GRU/RNN, Attention, Transformers, Residual Networks
• Good to have: Knowledge of optimization, Distributed training/computing, Language models
• Software peripherals:
• Must Have: REST services, SQL/NoSQL, UNIX, Code versioning
• Good to have: Docker containers, data versioning
• Research:
• Must Have: Well verse with latest trends in ML and DL area. Zeal to research and implement cutting areas in AI segment to solve complex problems
• Good to have: Contributed to research papers/patents and it is published on internet in ML and DL
  
  
  

Morningstar is an equal opportunity employer.

Morningstar’s hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. We’ve found that we’re at our best when we’re purposely together on a regular basis, at least three days each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you’ll have tools and resources to engage meaningfully with your global colleagues.

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