Job Summary (Primary function)
Reporting to the Associate Director, BIS, Data Engineering, the incumbent will focus on developing scalable machine learning, predictive analytics, and data engineering to build and productionize impactful models and workflows that drive informed decision-making across the Dermatology organization. This role will work closely with various business teams across the Dermatology business unit to translate business requirements into machine learning solutions, as well as independently manage end-to-end projects in a fast-paced environment.
Essential Functions of the Job (Key responsibilities)
Specific areas of responsibilities
·Design, build, and deploy machine learning and predictive models to solve business problems.
·Develop, implement, and maintain data pipelines using Azure Data Factory, connecting with various data sources, including blob storage and SQL databases.
·Actively contribute to and enforce data security and governance through tools or platforms such as Purview and Databricks
·Use Databricks to analyze, process, and transform large datasets, preparing them for machine learning models and reporting.
·Work with large language models (LLMs) like ChatGPT to integrate conversational AI features, leveraging API programming for seamless interaction with applications.
·Design and implement data models and work with graph databases to support predictive analytics and complex data relationships.
·Create interactive dashboards and applications with R Shiny for data visualization and decision-making support.
·Develop and maintain APIs to enable efficient data communication between various systems and applications.
·Collaborate with stakeholders to understand business requirements, presenting insights and model outcomes in a clear, actionable manner.
·Independently manage project timelines, deliverables, and documentation, ensuring alignment with business objectives and regulatory standards.
Qualifications (Minimal acceptable level of education, work experience, and competency)
·BS in Computer Science, Information Systems, Engineering, or a related field. Advanced degree or relevant certifications preferred.
·Must have a combined 3 – 5 years of hands-on experience building AI/ML solutions in industry and/or academia.
Technical Skills:
·Demonstrated proficiency in Python for data processing, machine learning model development, and API integration required.
·Experience in R and R Shiny for creating interactive data visualizations and applications.
·Experience with Azure Cloud Platform and Azure Data Factory for creating data pipelines and connecting to various data storage systems, including blob storage.
·Strong knowledge of Databricks for data engineering and model training in distributed computing environments.
·Familiarity with large language models (LLMs), such as ChatGPT, for building conversational AI and natural language processing applications.
·Proven experience with SQL databases, including Azure Synapse, for complex querying, data integration, and reporting.
·Knowledge of graph databases and data modeling to support predictive analytics and relational data structures is a plus.
·Skilled in API programming to facilitate secure, efficient integration between systems.
·Competency in predictive modeling techniques and machine learning algorithms for real-world applications.
·Proficiency in data modeling techniques for structuring and organizing data to support analytics.
Soft Skills:
·Strong communication skills with the ability to translate technical insights for non-technical stakeholders.
·Project management experience to independently handle multiple projects, ensuring timely and high-quality delivery.
·Ability to work independently, demonstrating self-motivation and a proactive approach to problem-solving.
Disclaimer: The above statements are intended to describe the general nature and level of work performed by employees assigned to this job. They are not intended to be an exhaustive list of all duties, responsibilities, and qualifications. Management reserves the right to change or modify such duties as required.
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During the process, you may be asked to respond to questions that will screen out your application if you do not meet certain objective criteria required by the job. You can learn more about this process .
