- Experience of model development using Python/PySpark libraries. Development on Databricks or Dataiku DSS (Data Science Studio) environment would be a plus
- Strong experience on Spark with Scala/Python/Java
- Strong proficiency in building/training/evaluating state of the art machine learning models and its deployment
- Proficiency in Statistical and Probabilistic methods such as SVM, Decision-Trees, Bagging and Boosting Techniques, Clustering.
- Proficiency in Core NLP techniques like Text Classification, Named Entity Recognition (NER), Topic Modeling, Sentiment Analysis, etc. Understanding of and experience with LLMs, LSTMs, GRUs, transformers. Familiarity with recommender systems, reinforcement learning.
- Hands on experience in Python data-science and math packages such as NumPy, Pandas, Sklearn, Seaborn, PyCaret, Matplotlib
- Proficiency in Python and common Machine Learning frameworks (TensorFlow, NLTK, Stanford NLP, PyTorch, Ling Pipe, Caffe, Keras, SparkML and OpenAI etc.)
- Experience of working in large teams and using collaboration tools like GIT, Jira and Confluence
- Good understanding of any of the cloud platform – AWS, Azure or GCP
- Understanding of Commercial Pharma landscape and Patient Data / Analytics would be a huge plus
- Should have an attitude of willingness to learn, accepting the challenging environment and confidence in delivering the results within timelines. Should be inclined towards self motivation and self-driven to find solutions for problems.
- Logical Thinking – Able to think analytically, use a systematic and logical approach to analyze data, problems, and situations. Be able to notice and call out discrepancies and inconsistencies in information and materials.
Task Management – Should have experience in task management and be able to plan self and team’s tasks. Should be able to proactively coarse-correct, basis the current priorities along with their tracking and progress report
Communication – Able to convey ideas and information clearly and accurately across forums/team in written or verbal
