Position Summary:

The primary purpose of this position is to develop data science projects, this spans across descriptive, predictive and prescriptive analytics depending on the business need.

The employee must be passionate about supporting evidence-based decision making and driven to investigate patterns and relationships within data using systematic and scalable methods.

Duties and Responsibilities:

1. Identify evidence needs and recommend data solutions; ask the right scientific questions, understand the evidence needs for research and development, regulatory and market access, and identify and make recommendations on fit-for-purpose data and analytics solutions.


2. Dive into data: Develop a comprehensive and deep understanding of the data we work with using AWS analytical tools and applications to broaden data accessibility and advance our proficiency/efficiency in understanding and using the data appropriately.


3. Be an expert in applying methods: Stay current with and adopt emergent analytical methodologies, tools and techniques to ensure fit-for-purpose and impactful approaches.


4. Produce high quality analyses: Apply rigor in study design and analytical methods; plan for data processing; design a fit-for-purpose analysis plan, assess effective ways of presenting and delivering the results to maximize impact and interpretability; implement and/or oversee the study, including its reporting; ensure compliance with applicable pharma industry regulations and standards.


5. Interpret and share results: Communicate findings to the rest of the team, internal stakeholders and regulatory body if needed.


6. Demonstrate strong collaboration skills and excellent communication skills to work with a global virtual team.


7. Code with integrity. Demonstrate a commitment to documentation, review, and testing.


8. Keep up to date with standards for data privacy to ensure integrity and accountability in all aspects of analytics and machine learning.


9. Take an approach to analysis that takes the right balance of privacy for the data subject vs getting the best result.


10. Coordinate analytical activities with the broader activities within the Global Data team.


11. Be able to use NLP with the need to classify both labelled and unlabelled data.

General Education and Experience:

1. Education – Bachelor’s degree in Science Mathematics or equivalent.

2. Two years’s experience of:

• SQL (postgresql, redshift, hive etc.)


• Python


• Workflow software’s such as KNIME, Alteryx for deployment

3. Knowledge of:

• Spark or PySpark


• Natural language processing toolkits like NLTK, spaCy


• Deep learning frameworks like Keras, TF Pytorch, Caffe


• Embedding models (word2vec, BERT)