Position Summary

Data Scientist with valuable 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
Positive 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 setting 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





Education Bachelor of Engineering in Statistics

Work Experience
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)

Behavioural Competencies Teamwork & Leadership Motivation to Learn and Grow

Ownership

Cultural Fit

Project Management

Communication

Technical Competencies

Python

R

SQL

EXCEL

MMx

Forecasting

Machine Learning

Pharma Commercial Know How

HEOR EPI and Economic Analysis

HEOR Simulation Analysis

Patient Data Analytics Know How

Dataiku

KNIME

Others

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