Highmark Health | Lead Big Data Engineer (MLOPs) – ML Ops Data Science R&D | Pennsylvania | United States | BigDataKB.com | 13 Oct 2022

Company :

Highmark Health

Job Description :

JOB SUMMARY

MLOps team at Highmark Health manages the operation of enterprise ML platforms and infrastructure used by data scientist, focuses on the development, support, best practices, policies, procedures and governance of ML models, model lifecycle management, and builds associated engineering processes designed to deploy ML and AI models efficiently and reliably in production on cloud and on prem. As a member of the MLOps team, your key responsibilities include enabling data scientists deploy models to cloud based systems, maintain machine learning and AI engineering processes (MLOps) that are deployed to cloud, building processes to monitor production ML systems, and setting up low latency and high throughput feature stores for real time scoring pipelines. Duties will include owning CI/CD application deployment pipelines for ML models, developing MLOps processes for model monitoring, model drift, evaluation of new ML tools and data science methods, and their selection and introduction to the wider organization, education and onboarding of Data Science teams to new technologies, participation in model and data code review, and horizon scanning to help maintain Highmark Health Advanced Analytics at the forefront of capability.

ESSENTIAL RESPONSIBILITIES

• In partnership with other business, platform, technology, and analytic teams across the enterprise, provide leadership in initiatives aimed at the design, build and maintenance of well-engineered data solutions in a variety of environments, including traditional data warehouses, Big Data solutions, and cloud-oriented platforms. Create high performance cloud and big data systems to be used with operational and analytic applications.
• Lead collaboration and projects with internal and external platforms and systems to reach alignment on data sourcing, flow, structure, and subject matter expertise. Lead discussions with business stakeholders and strategic partners to implement and support operational and analytic platforms. This may include products purchased by the organization that must be ingested or modeled/derived data maintained by enterprise platforms and data consumers.
• Working across multiple, disparate systems and platforms, design, code, test, implement, and maintain scalable and extensible frameworks that support data engineering services.
• Align with security, data governance and data quality programs by driving assigned components of metadata management, data quality management, and the application of business rules. Develop and maintain associated data engineering processes and participate in required operating procedures as part of the enterprise’s overall information management activities. Includes data cleansing, standardization, technical metadata documentation, and the de-identification and/or tokenization of data.
• Develop, optimize and/or maintain machine learning and AI engineering processes (MLOps) that are deployed to cloud or big data environments. These may be based on prototypes built by data scientists or capability frameworks implemented to allow data scientists to build efficiently in production environments. Work closely with architects, data scientists, and engineers to create robust, automated patters by which these solutions can be deployed and managed.
• Develop and assign tasks to teams across multiple projects with limited need for guidance. This includes providing guidance and education to Senior, Intermediate and Junior contributors within team. Manage relationships with customers of the function. Attend meetings with customers on a stand-alone basis or with team as needed.
• Establish standards and patterns for high performance data ingestion, transformation, and delivery of data analytic needs. Keep current with Big Data and cloud technologies in order to recommend best tools in order to perform current and future work.
• Other duties as assigned or requested.

EDUCATION

Required

• Bachelor’s Degree in Software Engineering, Information Systems, Computer Science, Data Science or related field

Substitutions

• None

Preferred

• Master’s Degree in Software Engineering, Information Systems, Computer Science, Data Science or related field

EXPERIENCE

Required

• 7 years of Data platform development, data engineering, software development, or data science
• 5 years of Big data or cloud data platform

Preferred

• 1+ years in DevOps and CI/CD application deployment pipelines to cloud including serverless, container orchestration, and cloud-based virtual machines
• Experience with data science model development on least one of the major cloud platforms: Google Cloud Platform, Amazon Web Services, and/or Microsoft Azure
• 1-2 years in Software Engineering with strong proficiency in Python programming
• Understanding of end-to-end DS model development and deployment lifecycle
• Knowledge of Terraform, Docker and Kubernetes, workflow orchestration tools like Kubeflow, Airflow, Vertex Pipelines
• Strong comfort with Linux/Unix shell
• High level of comfort working with Git

LICENSES AND CERTIFICATIONS

Required

• None

Preferred

• GCP Professional Cloud Developer or GCP Professional Data Engineer or GCP Professional Machine Learning Engineer or GCP Professional Cloud Devops Engineer or equivalent
• AWS Certified Developer Associate or AWS Certified Machine Learning Specialty or AWS Certified DevOps engineer or equivalent
• Microsoft Certified: Azure Data Scientist Associate or Microsoft Certified: Azure AI Engineer Associate or equivalent

SKILLS

• SQL
• Data Warehousing
• Problem-Solving
• Communication Skills
• Analytical Skills
• Spark or Python or related tool
• Cloud Technologies

Language (Other than English)

None

Travel Required

0% – 25%

PHYSICAL, MENTAL DEMANDS and WORKING CONDITIONS

Position Type

Remote

Teaches / trains others regularly

Rarely

Travel regularly from the office to various work sites or from site-to-site

Does Not Apply

Works primarily out-of-the office selling products/services (sales employees)

Never

Physical work site required

No

Lifting: up to 10 pounds

Constantly

Lifting: 10 to 25 pounds

Occasionally

Lifting: 25 to 50 pounds

Rarely

Disclaimer: The job description has been designed to indicate the general nature and essential duties and responsibilities of work performed by employees within this job title. It may not contain a comprehensive inventory of all duties, responsibilities, and qualifications required of employees to do this job.

Compliance Requirement: This job adheres to the ethical and legal standards and behavioral expectations as set forth in the code of business conduct and company policies.

As a component of job responsibilities, employees may have access to covered information, cardholder data, or other confidential customer information that must be protected at all times. In connection with this, all employees must comply with both the Health Insurance Portability Accountability Act of 1996 (HIPAA) as described in the Notice of Privacy Practices and Privacy Policies and Procedures as well as all data security guidelines established within the Company’s Handbook of Privacy Policies and Practices and Information Security Policy.

Furthermore, it is every employee’s responsibility to comply with the company’s Code of Business Conduct. This includes but is not limited to adherence to applicable federal and state laws, rules, and regulations as well as company policies and training requirements.

Highmark Health and its affiliates prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, age, religion, sex, national origin, sexual orientation/gender identity or any other category protected by applicable federal, state or local law. Highmark Health and its affiliates take affirmative action to employ and advance in employment individuals without regard to race, color, age, religion, sex, national origin, sexual orientation/gender identity, protected veteran status or disability.

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