Specialist - Data Sciences

Position Summary

This role will be responsible for translating analytics and generating insights from data with high level of autonomy Individual is expected to have strong technical expertise in data science and analytics space generating insights from data and providing solutions to the business as needed

Principal Responsibilities

Execute clinical and operation advanced analytics that facilitates gaining deep insight to clinical and operation data for relevant business partners in Global Development GD and clinical therapeutic areas

Develop novel data science methodologies to generate deep insights from disparate data sources

Provide subject matter expertise to teams in identification and application of the right data science methodology for a business problem

Formulate the right business problem in collaboration with business and create necessary analysis plans

Develop machine learning methodologies both supervised and unsupervised as appropriate to the business problem

Develop and apply statistical modeling methodologies including predictive and exploratory approaches to generate insights and support evidencebased decision making

Understand legacy R pipelines help refactor and develop scalable and robust code in R or Python in support of data science projects

Ensure the life cycle management of the models is maintained through code repositories and version control tools

Perform data engineering data preprocessing and data wrangling activities utilizing different approaches such as SQL NOSQL graph models

Good understanding of Natural Language Processing methodologies

Establishing an internal and external benchmarking network facilitating the distillation of best data integration and analytics practice from leading organization externally while also identifying best practice from internal teams within JJ

Pretrain fine tune and prompt engineering understanding leveraging LLMs

Design evaluate and implement Generative AI solutions leveraging large language models LLMs embeddings vector databases RetrievalAugmented Generation RAG and agentic workflows to solve clinical and operational business problems

Establish evaluation benchmarking and validation frameworks for statistical models machine learning solutions and GenAI applications to ensure performance robustness and business impact

Partner with Global Clinical Development business stakeholders SMEs and technology teams to identify opportunities where statistical analytics machine learning and GenAI can create value

Education and Experience Requirements

Individual should have a minimum of 2 years with relevant masters or Bachelors degree in data science statistics Informatics operations research computer science engineering or information systems or a related discipline OR

A minimum of 1 years with a PhD degree in data science statistics Informatics operations research computer science engineering or information systems is required

Other Relevant Experiences and Skills

Proven ability to translate and influence business requirements against existing analytics and data integration capabilities whilst identifying capabilities challenges and necessary solutions to ensure realistic goal setting for future analytics modelling

A proven record in shaping and delivering key programs that optimized performance and delivered project support in matrix environments is required

Experience in advanced analytics processes including gaining insight from structured and unstructured data predictive modelling and optimization and associated systemsdatabase infrastructure

Strong working knowledge of traditional statistical methodologies

Strong working knowledge of statistical modeling techniques including regression classification survival analysis experimental design and predictive analytics

Strong working knowledge of machine learning methodologies both supervised and unsupervised learning methods

Experience applying and evaluating Generative AI technologies including LLMs prompt engineering embeddings vector databases RetrievalAugmented Generation RAG and multimodal AI systems

High proficiency in one of the programming languages R and statistical pipelines

Familiarity of best practices in machine learning model development validation and deployment

Familiarity with evaluation and benchmarking methodologies for machine learning and GenAI solutions including model validation performance assessment and AI governance principles

Solid understanding of life cycle management of the code and code repositories

Extensive experience with data wrangling data preprocessing and data engineering techniques on large datasets

Technical expertise preferably in pharmaceutical clinical or a regulated industry operation with extensive ex