iLink Digital
Technical Lead - Machine Learning and Data Science
iLink Digital, Dallas, Texas, United States, 75215
Technical Lead - Machine Learning and Data Science
Dallas, United States
| Posted on 08/21/2024iLink is a GlobalSoftware Solution Provider and Systems Integrator, deliversnext-generation technology solutions to help clients solvecomplex business challenges, improve organizationaleffectiveness, increase business productivity, realizesustainable enterprise value and transform your businessinside-out. iLink integrates software systems and developscustom applications, components, and frameworks on the latestplatforms for IT departments, commercial accounts, applicationservices providers (ASP) and independent software vendors(ISV). iLink solutions are used in a broad range of industriesand functions, including healthcare, telecom, government, oiland gas, education, and life sciences. iLink’s expertiseincludes Cloud Computing & Application Modernization, DataManagement & Analytics, Enterprise Mobility, Portal,collaboration & Social Employee Engagement, EmbeddedSystems and User Experience designetc.
What makes iLinkSystems' offerings unique is the fact that we usepre-created frameworks, designed to accelerate softwaredevelopment and implementation of business processes for ourclients. iLink has over 60 frameworks (solution accelerators),both industry-specific and horizontal, that can be easilycustomized and enhanced to meet your current businesschallenges.
Requirements
Job Description:
We are seeking an experienced and highly skilled Technical Lead inMachine Learning and Data Science to join our team. As a Technical Lead, youwill play a pivotal role in driving the development and deployment of machinelearning models based on electronic health records (EHR) information. Yourresponsibilities will include leading the data science team, collaboratingwith stakeholders, and ensuring successful production deployment of machinelearning models in the Azure cloud environment.
Key Responsibilities:
1. Leadership and Team Management:
Leadand mentor a team of data scientists and machine learning engineers.
Defineproject goals, timelines, and deliverables, and ensure they are met.
Fostera culture of collaboration, innovation, and continuous learning withinthe team.
UtilizeNatural Language Processing (NLP) techniques, including tools such asSpaCy and NER (Named Entity Recognition), to extract insights fromunstructured EHR data.
Developand implement classification algorithms for categorizing EHRinformation.
Buildtopic modeling classifiers to identify key themes and trends inhealthcare data.
UtilizeDeep Learning techniques for advanced data analysis and prediction.
Workclosely with Azure ML Workbench to develop, test, and deploy machinelearning models in the Azure cloud environment.
Implementscalable and reliable production pipelines for model deployment andmonitoring.
Collaboratewith DevOps teams to ensure smooth integration of machine learningmodels into existing healthcare systems.
4. Performance Optimization and Model Evaluation:
Optimizemachine learning models for performance, scalability, and accuracy.
Conductrigorous testing and validation to ensure the quality of deployedmodels.
Monitormodel performance in production and implement improvements as needed.
5. Stakeholder Collaboration:
Collaboratewith healthcare professionals, data analysts, and business stakeholdersto understand requirements and goals.
Translatebusiness needs into technical solutions and actionable insights.
Qualifications:
Master'sor Ph.D. in Computer Science, Data Science, Statistics, or a relatedfield.
10+years of experience in data science, machine learning, and AI.
Strongexpertise in NLP techniques, including text preprocessing, entityrecognition, and sentiment analysis.
Proficiencyin machine learning tools and libraries such as SpaCy, TensorFlow,PyTorch, scikit-learn, and Azure ML.
Experiencebuilding and deploying machine learning models in the Azure cloudenvironment.
Familiaritywith DevOps practices and tools for continuous integration anddeployment (CI/CD).
Excellentproblem-solving skills and the ability to work in a fast-paced,collaborative environment.
Strongcommunication and leadership skills, with a track record of successfullyleading data science projects from conception to production.
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Dallas, United States
| Posted on 08/21/2024iLink is a GlobalSoftware Solution Provider and Systems Integrator, deliversnext-generation technology solutions to help clients solvecomplex business challenges, improve organizationaleffectiveness, increase business productivity, realizesustainable enterprise value and transform your businessinside-out. iLink integrates software systems and developscustom applications, components, and frameworks on the latestplatforms for IT departments, commercial accounts, applicationservices providers (ASP) and independent software vendors(ISV). iLink solutions are used in a broad range of industriesand functions, including healthcare, telecom, government, oiland gas, education, and life sciences. iLink’s expertiseincludes Cloud Computing & Application Modernization, DataManagement & Analytics, Enterprise Mobility, Portal,collaboration & Social Employee Engagement, EmbeddedSystems and User Experience designetc.
What makes iLinkSystems' offerings unique is the fact that we usepre-created frameworks, designed to accelerate softwaredevelopment and implementation of business processes for ourclients. iLink has over 60 frameworks (solution accelerators),both industry-specific and horizontal, that can be easilycustomized and enhanced to meet your current businesschallenges.
Requirements
Job Description:
We are seeking an experienced and highly skilled Technical Lead inMachine Learning and Data Science to join our team. As a Technical Lead, youwill play a pivotal role in driving the development and deployment of machinelearning models based on electronic health records (EHR) information. Yourresponsibilities will include leading the data science team, collaboratingwith stakeholders, and ensuring successful production deployment of machinelearning models in the Azure cloud environment.
Key Responsibilities:
1. Leadership and Team Management:
Leadand mentor a team of data scientists and machine learning engineers.
Defineproject goals, timelines, and deliverables, and ensure they are met.
Fostera culture of collaboration, innovation, and continuous learning withinthe team.
UtilizeNatural Language Processing (NLP) techniques, including tools such asSpaCy and NER (Named Entity Recognition), to extract insights fromunstructured EHR data.
Developand implement classification algorithms for categorizing EHRinformation.
Buildtopic modeling classifiers to identify key themes and trends inhealthcare data.
UtilizeDeep Learning techniques for advanced data analysis and prediction.
Workclosely with Azure ML Workbench to develop, test, and deploy machinelearning models in the Azure cloud environment.
Implementscalable and reliable production pipelines for model deployment andmonitoring.
Collaboratewith DevOps teams to ensure smooth integration of machine learningmodels into existing healthcare systems.
4. Performance Optimization and Model Evaluation:
Optimizemachine learning models for performance, scalability, and accuracy.
Conductrigorous testing and validation to ensure the quality of deployedmodels.
Monitormodel performance in production and implement improvements as needed.
5. Stakeholder Collaboration:
Collaboratewith healthcare professionals, data analysts, and business stakeholdersto understand requirements and goals.
Translatebusiness needs into technical solutions and actionable insights.
Qualifications:
Master'sor Ph.D. in Computer Science, Data Science, Statistics, or a relatedfield.
10+years of experience in data science, machine learning, and AI.
Strongexpertise in NLP techniques, including text preprocessing, entityrecognition, and sentiment analysis.
Proficiencyin machine learning tools and libraries such as SpaCy, TensorFlow,PyTorch, scikit-learn, and Azure ML.
Experiencebuilding and deploying machine learning models in the Azure cloudenvironment.
Familiaritywith DevOps practices and tools for continuous integration anddeployment (CI/CD).
Excellentproblem-solving skills and the ability to work in a fast-paced,collaborative environment.
Strongcommunication and leadership skills, with a track record of successfullyleading data science projects from conception to production.
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