Machine Learning Engineering, Senior Specialist

Role Summary

This role leads and executes development and programming of machine learning integrated software algorithms, structures, analyzes, and leverages data in a production environment. Mentors and develops junior machine learning engineering analysts, and is an expert in machine learning models, deployment architecture, and advanced techniques such as NLP/NLU. This role also includes creating data mining architectures, applying data science methodologies, and researching emerging techniques to identify trends and inform business decisions.

Responsibilites
1. Leads complex model development and deployment pipelines. Establishes best practices and drives innovation in data preparation and consumption. Expert in end-to-end data and model pipeline deployment and automation technologies.
2. Integrates and optimizes existing complex data and model pipelines in a production environment. Identifies and diagnoses data inconsistencies and errors, documents assumptions, and forages to fill data gaps. Applies expert knowledge of experimental methodologies, statistics, optimization, probability theory, and machine learning concepts to create self-running artificial intelligence (AI) systems to automate predictive models. Expert level knowledge in SDLC and related tools and processes. Serves as a thought partner for Machine Learning Operations team.
3. Leads engineering design for complex data pipeline designs. Proficient in developing highly efficient designs and strategy for both batch and real-time data pipelines.
4. Partners with data science teams to understand data requirements, performs data discovery for model development. Performs detailed analysis of raw data sources for data quality, applies business context, and model development needs. Drives best practices and innovation in data discovery techniques.
5. Engages with internal stakeholders to understand and probe business processes and develop hypotheses. Brings structure to requests and translates requirements into an analytic approach. Participates in and influences ongoing business planning and departmental prioritization activities.
6. Writes model monitoring scripts as needed. Diagnoses root causes based on model monitoring alerts and triages issues. Coordinates and plans response to model monitoring alerts and resolves issues.
7. Serves as a machine learning engineering subject matter expert on cross functional teams for large strategic initiatives and contributes to the growth of the Vanguard analytic community. Serves as data and Machine Learning engineering thought partner for the enterprise.
8. Participates in special projects and performs other duties as assigned.
9. Designs advanced analytical protocols for mining large-scale datasets to uncover predictive patterns and optimize strategic initiatives.
10. Applies big data modeling techniques across domains such as market intelligence, advertising, and scientific research to inform critical business decisions.
11. Explores cutting-edge AI and data science frameworks to develop scalable, next-gen machine learning architectures.

Qualifications and Skills

• Undergraduate degree or equivalent combination of training and experience. Graduate degree preferred.
  Minimum of ten years related work experience.
• Deep expertise in ML pipeline design, big data architecture, real-time data engineering, model automation, production deployment, and statistical analysis frameworks

Special Factors

Sponsorship

Vanguard is not offering visa sponsorship for this position.

About Vanguard

At Vanguard, we don't just have a mission—we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.