Position 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
- Valuable 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





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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