Job Summary: -  

Data Scientist with good hands-on experience of 3+ years in developing state of the art and scalable Machine Learning models and their operationalization, leveraging off-the-shelf workbench production. 

 

Job Responsibilities: -

 

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. Should be able to mentor and guide mid to large sized teams under him/her

 

Job Requirements: -

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 Generative AI / Large Language Models / Transformers would be a plus 

 

 

 

Qualification: -

 

B-Tech or BE in Computer Science / Computer Applications from Tier 1-2 college with 3+ years of proven experience in the field of Advanced Analytics or Machine Learning

OR

Master’s degree in Machine Learning / Statistics / Econometrics, or related discipline from Tier 1-2 college with 3+ years of experience

 

Must have Skills: -

 

Real-world experience in implementing machine learning/statistical/econometric models/advanced algorithms Breadth of machine learning domain knowledge Experience in application of machine learning algorithms (classification, regression, deep learning, NLP, etc.) Experience with a ML/data-centric programming language (such as Python, Scala, or R) and ML libraries (pandas, numpy, scikit-learn, etc.) Experience with Apache Hadoop / Spark (or equivalent cloud-computing/map-reduce framework)

 

Skills that give you an edge: -

Strong analytical skills to solve and model complex business requirements are a plus. With life sciences or pharma background.