Data Scientist

Experience range : More than 8 yrs as wellas 1012yrs candidates who are interested in individual contributorrole.
Key Skills(Mandatory)
NLPTimeseries ML and DeeplearningSQLAzurecloud services work exGood pythonknowledgeDebugging skills inPythonExperience in Pytorch orTensorflowProductionization ofmodelsData visualization tools likePowerBI
Detailed JD:
Overallresponsibilities
Businessand data understanding: Working with other data scientists you knowhow to build your understanding of the business context systemsenvironment strategy and people aligned to the given area. Thisalso includes an understanding of the relevant internal andexternal highvolume data sources validity and domains of such datato align with the intended business objective. You will conductdata acquisition from multiple data sources and wrangle data setsselecting appropriate techniques such as parsing or an algorithm tocreate a data structure relevant to the problem. This can includebut not limited to techniques such as ETL batch processingstreaming ingestion scrapers APIs and crawlers. NOTE: adjust herebased on your teams process of identifying DS project scope. Couldalso be the case for example After receiving an explorative briefyou will build your understanding of the business context systemsenvironment strategy and people to convert it into a valueddeliverable. Data preparation: You willcreate required data set(s) utilizing your understanding of complexbusiness problems data formats applicability of the data to theproblem and standard modelling techniques. You can performtechniques to validate the quality of the data and can fuse datafrom various sources using knowledge of data preprocessingtechniques.Modelling: For the intendedbusiness objective you can utilize advanced modelling techniquessuch as predictive modelling advanced clustering association rulesto create a model or models that perform to particular requirementsand specifications. You are able to anticipate how a model srequirements or the available data might change over time and beable to design a product that will be able to cope with theserequirements. Test & validate themodel: You are able to apply best practice model fit testing tuningand validation techniques such as chi square ROC curve root meansquare error to assess model performance compare results fromvarious methodologies and suggest improvements. You both give andseek feedback and ideas with others from across differing businessareas as relevant to create better products and continually advanceyour data science skills. Communication ofinsights: You are able to design rich data visualisations and usethem to communicate complex messages to business leaders in a clearand simple way with the ability to respond effectively tochallenges. In the role youwill
Work ina stimulating and supportive environment; Be part of a dynamicgrowing community of data analysts and data scientists at GSK; Havecareerlong access to our GSK Data Academy a dedicated tailoredlearning programme that blends your onthejob learning with targetedcoaching peertopeer mentoring expertled workshops and a network oflearning providers; Work on high impact strategic priorities;deploy GSKs large varied datasets to tackle analytics challenges;Be at the forefront of GSKs data and analyticstransformation
Coreand Technical Competencies to be successful in this role youshould:
Becurious and passionate about creative thinking and data innovationenjoy problem solving and have empathy for the problems you arechallenged to solve. Be accountable fordriving projects and improvement activities through value deliveryfor GSK leaders from across different businessareas;Be able to compile integrate andanalyze data from multiple sources to answer business questions.Have the ability to conceptualize formulateprototype and implement algorithms to solve complex businessproblems. Demonstrate the ability tocommunicate complex data science concepts to a variety ofstakeholders. Have the ability to plan andexecute value measurement of data work in order to demonstrate andimprove results.Possess practical experiencedeveloping and implementing machine learning on large data sets.Have knowledge in mining large &complex data sets using R Python SQL etc. from a multitude ofenvironments such as cloud Hadoop clusters NoSQL or Spark(Understand statistical modelling techniquesand the mathematical foundations of applied ML and AI algorithmsand models.
BasicQualifications
Minimum BS degree in STEM subjects (Computer Science MachineLearning Artificial Intelligence Statistics BioinformaticsEngineering MathematicsChemistry)
Total 8 to 14 yers of IT relevant experience from reputed ITcompany
68years of experience of working in Data Science or relatedrole.Hands on experience on any CloudPlatform preferable Azure Working knowledgecloud based and local data science frameworks and toolkits.
Workingknowledge and experience of project management methodologiesinclude Agile methodologies and the hypothesisdriven approach
Have apractical understanding of Machine Learning Deep Learning andnatural language understanding/processing or speech recognition (ifapplicable)
Experience of building large scale machine learning systems (ifapplicable)
Exposure to Cuda pyTorch or TensorFlow. (fill in asapplicable)
Nice to have experience of working in Finance/ Procurementdomain.
Overallresponsibilities
Businessand data understanding: Working with other data scientists you knowhow to build your understanding of the business context systemsenvironment strategy and people aligned to the given area. Thisalso includes an understanding of the relevant internal andexternal highvolume data sources validity and domains of such datato align with the intended business objective. You will conductdata acquisition from multiple data sources and wrangle data setsselecting appropriate techniques such as parsing or an algorithm tocreate a data structure relevant to the problem. This can includebut not limited to techniques such as ETL batch processingstreaming ingestion scrapers APIs and crawlers. NOTE: adjust herebased on your teams process of identifying DS project scope. Couldalso be the case for example After receiving an explorative briefyou will build your understanding of the business context systemsenvironment strategy and people to convert it into a valueddeliverable. Data preparation: You willcreate required data set(s) utilizing your understanding of complexbusiness problems data formats applicability of the data to theproblem and standard modelling techniques. You can performtechniques to validate the quality of the data and can fuse datafrom various sources using knowledge of data preprocessingtechniques.Modelling: For the intendedbusiness objective you can utilize advanced modelling techniquessuch as predictive modelling advanced clustering association rulesto create a model or models that perform to particular requirementsand specifications. You are able to anticipate how a model srequirements or the available data might change over time and beable to design a product that will be able to cope with theserequirements. Test & validate themodel: You are able to apply best practice model fit testing tuningand validation techniques such as chi square ROC curve root meansquare error to assess model performance compare results fromvarious methodologies and suggest improvements. You both give andseek feedback and ideas with others from across differing businessareas as relevant to create better products and continually advanceyour data science skills. Communication ofinsights: You are able to design rich data visualisations and usethem to communicate complex messages to business leaders in a clearand simple way with the ability to respond effectively tochallenges. Core and Technical Competenciesto be successful in this role youshould:
Becurious and passionate about creative thinking and data innovationenjoy problem solving and have empathy for the problems you arechallenged to solve. Be accountable fordriving projects and improvement activities through value deliveryfor GSK leaders from across different businessareas;Be able to compile integrate andanalyze data from multiple sources to answer business questions.Have the ability to conceptualize formulateprototype and implement algorithms to solve complex businessproblems. Demonstrate the ability tocommunicate complex data science concepts to a variety ofstakeholders. Have the ability to plan andexecute value measurement of data work in order to demonstrate andimprove results.Possess practical experiencedeveloping and implementing machine learning on large data sets.Have knowledge in mining large &complex data sets using R Python SQL etc. from a multitude ofenvironments such as cloud Hadoop clusters NoSQL or Spark(Understand statistical modelling techniquesand the mathematical foundations of applied ML and AI algorithmsand models. BasicQualifications
Minimum BS degree in STEM subjects (Computer Science MachineLearning Artificial Intelligence Statistics BioinformaticsEngineering MathematicsChemistry)
Total 8 to 14 yers of IT relevant experience from reputed ITcompany
68years of experience of working in Data Science or relatedroleHands on experience on any CloudPlatform preferable Azure Working knowledgecloud based and local data science frameworks and toolkits.
Workingknowledge and experience of project management methodologiesinclude Agile methodologies and the hypothesisdriven approach
Have apractical understanding of Machine Learning Deep Learning andnatural language understanding/processing or speech recognition (ifapplicable)
Experience of building large scale machine learning systems (ifapplicable)
Exposure to Cuda pyTorch or TensorFlow. (fill in asapplicable)
Nice to have experience of working in Finance/ Procurementdomain.
PreferredQualifications
Advanceddegree in STEM subjects (Computer Science Machine LearningArtificial Intelligence Statistics Bioinformatics EngineeringMathematics Chemistry)Experience in handlinglarge enterprise implementation of algorithms anddata.Add other tools systemsetc. powerbi,pytorch,tensorflow,timeseries,azurecloud services,sql,machine learning,data visualization tools,deeplearning,productionization,nlp,python,ml

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