JOB SUMMARY
Outcomes™ is a digital ecosystem connecting Payers, Pharmaceutical manufacturers, and Pharmacies, to enhance patient outcomes and close gaps in healthcare system. Outcomes provides Medication Adherence, and Medication Therapy Management (MTM) services through various digital channels. With 60K connected pharmacies and 400M patient engagements, there are ample opportunities to harness your creativity to reduce healthcare costs and improve patientcare.
As a Senior Data and ML Engineer, you will be responsible for defining and implementing cloud-based Data Warehouses, Analytics, and Machine Learning solutions. In this key role, your main objective will be to architect, implement, and operationalize large-scale Data and Analytics solutions utilizing AWS Cloud technologies such as RedShift, Data Glue, SageMaker, Bedrock, and third-party ML models. Serving as a subject matter expert, you will engage in hands-on implementation and offer consultative support across various build and run operations.
This role is part of the Data Services team, utilizing cutting-edge cloud technologies to develop Data Platforms and Analytics Solutions, including Machine Learning initiatives that support the Outcomes Business. In this position, you will collaborate with data analysts, product engineers, and business stakeholders to create dependable data pipelines, convert raw data into meaningful business metrics and KPIs, and provide predictions/insights through various Analytics models.
You’ll thrive in small, cross-functional teams that embrace agile methodologies, follow data best practices and standards, and focus on driving automation and self-service capabilities to democratize data throughout the organization.
At Outcomes, Inc., we are looking for a forward-thinking Sr. Engineer with a strong sense of ownership, initiative, and a passion for transforming our business into a data-driven enterprise by modernizing our data analytics landscape.
Essential Duties And Responsibilities
- Participate in Data and ML discovery activity to understand feasibility of new ML use cases
- Work on use case feasibility discussions and translate business ideas / needs to user stories
- Lead exploratory data analysis for various use cases to ensure robust ML implementations that can provide reliable predictions
- Work with business users and other subject matter experts to implement Data & ML solutions
- Understand business problems/needs and propose solutions.
- Design or suggest improvements to optimize existing solutions
- Implement and test the solution to ensure results or predictions meet the business criteria
- Deploy and enhance the solutions
- Operationalize Data & ML Solutions
- Develop and maintain complex Data, ML Pipelines and Models
- Build and enhance Data and ML Pipelines to ingest data and build data features for modeling
- Develop and maintain CICD artifacts to support Data and ML Solutions
- Monitor and troubleshoot processes daily
- Document new and existing processes
- Support and maintain Data & ML solutions
- Troubleshot and resolve production issues
- Implement enhancements and bug fixes
- Optimize ML pipelines, ML Models and Business predictions
- Proactively and independently identify performance issues and recommend enhancements
- Proactively monitor and support production systems, Data and ML solutions including on-call responsibilities
- Work collaboratively with other Data Engineers to understand and contribute to the overall data architecture and pipelines
- Mentor and assist other team members on issues and methodology
- Develop software with a security-first mindset using knowledge of standard security protocols and common security risks, secure coding techniques, and appropriate usage and protection of sensitive information such as PHI. Identify potential security issues during requirements analysis, development, and threat modeling discussions.
Qualifications
KNOWLEDGE & REQUIREMENTS
- Experience with Data Engineering, Modeling and Data Warehousing technologies
- Experience with ML Engineering, Modeling and operationalizing ML Models
- Strong experience in ETL design, development, testing, maintenance, and implementation of data pipelines and analytics initiatives
- Strong SQL programming/python scripting/data analysis experience
- Develops ETL mapping specifications for loading information into the data warehouse and for ensuring reliability of information loaded.
- Experience in gathering requirements, development, testing and maintenance of Data and Analytics Solutions.
- Validate data to ensure accuracy and consistency and meets the requirements criteria
- Partners with business end-users to develop and implement collaborative solutions
- Knowledge with Qlik Replicate technologies is a plus
- Strong knowledge in AWS Cloud Data Services (AWS RedShift, Glue, SageMaker, and Bedrock)
- Experience using cloud services for concepts such as storage and compute. Capable of applying infrastructure as code practices.
- Provides a positive impact on team: influences team decisions, mentors peers, drives innovation
- Excellent written and verbal communication skills
- Experience with developing Chatbot automations is a plus
Desirable Qualifications
- Self-motivated, quick learner, and thrives in fast-paced environments while adapting seamlessly to new technologies
- Experience working with healthcare technology.
- Experience working with PHI data and HIPAA standards.
- Experience resolving issues that do not have clear answers.
- High sense of urgency and accountability
- Adaptable, friendly, and ability to work with teams.
- Passion for data and digging into the minutia of datasets.
Education & Experience Requirements
- Minimum years of work experience: 7 years
- Minimum level of education or education/experience: Bachelors or equivalent work experience in Computer Science, Computer Engineering, Electrical Engineering, Information Systems, IT
- 3 years of Data Warehousing experience
- 4 years of experience in build data and ML pipelines, and complex SQL scripting
- 5 years of experience in Data Engineering
