Senior Data Scientist

Role Summary

Working collaborating with a cross-functional team of Data Scientists, MLOPs Engineers, Solution Architects, Software Engineering Product Managers to help build an automated solution for data collection. Seamlessly deploy and operationalize models as scalable robust services which will include requirement understanding, model development, productionizing models, model serving, API/library/CLI development, developing data visualization tools, code refactoring, unit testing and support. As a Senior Data Scientist, you will be a leading contributor in the implementation of Artificial Intelligence (AI) within Data Collections software applications, API s , and other data products. This role requires significant interaction with both upstream and downstream stakeholders across Technology, Data, Products, Sales/Service, and Research.

• Data Collection and Cleaning : Gathering data from various sources and ensuring its quality by cleaning and organizing it.

• Data Analysis : Using statistical techniques and machine learning algorithms to analyze data and uncover patterns, trends, and insights.

• Model Building : Creating predictive models and algorithms to solve business problems and improve decision-making.

• Data Visualization : Presenting data insights through visualizations and reports to help stakeholders understand the findings.

• Collaboration : Working closely with business stakeholders to understand their goals and determine how data can be used to achieve them

Requirements:

• Experience in extracting data / information, through complex semi-structured and unstructured documents using NLP Parsing

• Analyzing 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.

• Good Understanding, Skills Hands-on Experience in:

Must Haves

• NLP, Scraping, Parsing including libraries such as NLTK, Gensim , Spacy, Scrapy, beautifulsoup , regex etc.

• Deep Learning including Keras , TensorFlow / PyTorch , Neural Networks, such as CNN, LSTM/GRU/RNN/CNN/GAN/Residual Networks etc.

• Supervised, unsupervised, semi-supervised, few shot / zero shot learning including EDA, training, modelling, hyper-parameter tuning, API creation etc. in Regression Binary/Multiclass classifications in algorithms such as Decision Trees, SVM, XGBoost etc.

• Python data structures using List, tuple, dictionary, collections, iterators, Pandas, NumPy etc. including libraries such as Scikit-learn, imblearn , SciPy etc.

• Basic Database SQL knowledge (like Postgres, SQL Server, MySQL etc )

Desirables

• AWS services like EC2, Beanstalk, Lambda including Containerization, Docker images etc.

• Generative AI, Transfer Learning, Transformers, Embeddings, LLMs, Prompt Engineering, Encoders, Decoders etc.

• Object oriented programing( OOP) Rest API

• CI/CD/CT, MLOps

How is it to work with Data collection AI team at Morningstar

You get to work on

1. Research work coupled with business value

2. Machine learning development Lifecyle, i.e. End to end project development (Not just POCs)

3. Exposure to advanced workspace on cloud environment

4. Encouragement for innovation and ideation

Experience: Min 4 to 7 yrs relevant experience in Data Science AI ML

Qualifications

• Full time Engineering Degree in C omputers or full Time Bachelor s degree in Mathematics / Statistics / Science from a recognized institution

• Advanced Professional Course or Certification in Data Science / Machine Learning

• Professional Course or Certification in Python