Data Scientist

Experience range : More than 8 yrs as well as 1012yrs candidates who are interested in individual contributor role.

Key Skills (Mandatory)

NLP Timeseries ML and Deep learning

SQL

Azure cloud services work ex

Good python knowledge

Debugging skills in Python

Experience in Pytorch or Tensorflow

Productionization of models

Data visualization tools like PowerBI

Detailed JD :

Overall responsibilities

Business and data understanding: Working with other data scientists you know how to build your understanding of the business context systems environment strategy and people aligned to the given area. This also includes an understanding of the relevant internal and external highvolume data sources validity and domains of such data to align with the intended business objective. You will conduct data acquisition from multiple data sources and wrangle data sets selecting appropriate techniques such as parsing or an algorithm to create a data structure relevant to the problem. This can include but not limited to techniques such as ETL batch processing streaming ingestion scrapers APIs and crawlers. NOTE: adjust here based on your teams process of identifying DS project scope. Could also be the case for example After receiving an explorative brief you will build your understanding of the business context systems environment strategy and people to convert it into a valued deliverable.

Data preparation: You will create required data set(s) utilizing your understanding of complex business problems data formats applicability of the data to the problem and standard modelling techniques. You can perform techniques to validate the quality of the data and can fuse data from various sources using knowledge of data preprocessing techniques.

Modelling: For the intended business objective you can utilize advanced modelling techniques such as predictive modelling advanced clustering association rules to create a model or models that perform to particular requirements and specifications. You are able to anticipate how a model s requirements or the available data might change over time and be able to design a product that will be able to cope with these requirements.

Test & validate the model: You are able to apply best practice model fit testing tuning and validation techniques such as chi square ROC curve root mean square error to assess model performance compare results from various methodologies and suggest improvements. You both give and seek feedback and ideas with others from across differing business areas as relevant to create better products and continually advance your data science skills.

Communication of insights: You are able to design rich data visualisations and use them to communicate complex messages to business leaders in a clear and simple way with the ability to respond effectively to challenges.

In the role you will

Work in a stimulating and supportive environment; Be part of a dynamic growing community of data analysts and data scientists at GSK; Have careerlong access to our GSK Data Academy a dedicated tailored learning programme that blends your onthejob learning with targeted coaching peertopeer mentoring expertled workshops and a network of learning providers; Work on high impact strategic priorities; deploy GSKs large varied datasets to tackle analytics challenges; Be at the forefront of GSKs data and analytics transformation

Core and Technical Competencies to be successful in this role you should:

Be curious and passionate about creative thinking and data innovation enjoy problem solving and have empathy for the problems you are challenged to solve.

Be accountable for driving projects and improvement activities through value delivery for GSK leaders from across different business areas;

Be able to compile integrate and analyze data from multiple sources to answer business questions.

Have the ability to conceptualize formulate prototype and implement algorithms to solve complex business problems.

Demonstrate the ability to communicate complex data science concepts to a variety of stakeholders.

Have the ability to plan and execute value measurement of data work in order to demonstrate and improve results.

Possess practical experience developing and implementing machine learning on large data sets.

Have knowledge in mining large & complex data sets using R Python SQL etc. from a multitude of environments such as cloud Hadoop clusters NoSQL or Spark (

Understand statistical modelling techniques and the mathematical foundations of applied ML and AI algorithms and models.

Basic Qualifications

Minimum BS degree in STEM subjects (Computer Science Machine Learning Artificial Intelligence Statistics Bioinformatics Engineering Mathematics Chemistry)

Total 8 to 14 yers of IT relevant experience from reputed IT company

68 years of experience of working in Data Science or related role.

Hands on experience on any Cloud Platform preferable Azure

Working knowledge cloud based and local data science frameworks and toolkits.

Working knowledge and experience of project management methodologies include Agile methodologies and the hypothesisdriven approach

Have a practical understanding of Machine Learning Deep Learning and natural language understanding/processing or speech recognition (if applicable)

Experience of building large scale machine learning systems (if applicable)

Exposure to Cuda pyTorch or TensorFlow. (fill in as applicable)

Nice to have experience of working in Finance/ Procurement domain.

Overall responsibilities

Business and data understanding: Working with other data scientists you know how to build your understanding of the business context systems environment strategy and people aligned to the given area. This also includes an understanding of the relevant internal and external highvolume data sources validity and domains of such data to align with the intended business objective. You will conduct data acquisition from multiple data sources and wrangle data sets selecting appropriate techniques such as parsing or an algorithm to create a data structure relevant to the problem. This can include but not limited to techniques such as ETL batch processing streaming ingestion scrapers APIs and crawlers. NOTE: adjust here based on your teams process of identifying DS project scope. Could also be the case for example After receiving an explorative brief you will build your understanding of the business context systems environment strategy and people to convert it into a valued deliverable.

Data preparation: You will create required data set(s) utilizing your understanding of complex business problems data formats applicability of the data to the problem and standard modelling techniques. You can perform techniques to validate the quality of the data and can fuse data from various sources using knowledge of data preprocessing techniques.

Modelling: For the intended business objective you can utilize advanced modelling techniques such as predictive modelling advanced clustering association rules to create a model or models that perform to particular requirements and specifications. You are able to anticipate how a model s requirements or the available data might change over time and be able to design a product that will be able to cope with these requirements.

Test & validate the model: You are able to apply best practice model fit testing tuning and validation techniques such as chi square ROC curve root mean square error to assess model performance compare results from various methodologies and suggest improvements. You both give and seek feedback and ideas with others from across differing business areas as relevant to create better products and continually advance your data science skills.

Communication of insights: You are able to design rich data visualisations and use them to communicate complex messages to business leaders in a clear and simple way with the ability to respond effectively to challenges.

Core and Technical Competencies to be successful in this role you should:

Be curious and passionate about creative thinking and data innovation enjoy problem solving and have empathy for the problems you are challenged to solve.

Be accountable for driving projects and improvement activities through value delivery for GSK leaders from across different business areas;

Be able to compile integrate and analyze data from multiple sources to answer business questions.

Have the ability to conceptualize formulate prototype and implement algorithms to solve complex business problems.

Demonstrate the ability to communicate complex data science concepts to a variety of stakeholders.

Have the ability to plan and execute value measurement of data work in order to demonstrate and improve results.

Possess practical experience developing and implementing machine learning on large data sets.

Have knowledge in mining large & complex data sets using R Python SQL etc. from a multitude of environments such as cloud Hadoop clusters NoSQL or Spark (

Understand statistical modelling techniques and the mathematical foundations of applied ML and AI algorithms and models.

Basic Qualifications

Minimum BS degree in STEM subjects (Computer Science Machine Learning Artificial Intelligence Statistics Bioinformatics Engineering Mathematics Chemistry)

Total 8 to 14 yers of IT relevant experience from reputed IT company

68 years of experience of working in Data Science or related role

Hands on experience on any Cloud Platform preferable Azure

Working knowledge cloud based and local data science frameworks and toolkits.

Working knowledge and experience of project management methodologies include Agile methodologies and the hypothesisdriven approach

Have a practical understanding of Machine Learning Deep Learning and natural language understanding/processing or speech recognition (if applicable)

Experience of building large scale machine learning systems (if applicable)

Exposure to Cuda pyTorch or TensorFlow. (fill in as applicable)

Nice to have experience of working in Finance/ Procurement domain.

Preferred Qualifications

Advanced degree in STEM subjects (Computer Science Machine Learning Artificial Intelligence Statistics Bioinformatics Engineering Mathematics Chemistry)

Experience in handling large enterprise implementation of algorithms and data.

Add other tools systems etc.