채용
Benefits & Perks
•Flexible Hours
•Flexible Hours
Required Skills
Machine Learning
Data Pipeline Development
Python
AWS
Model Deployment
Statistics
Core Responsibilities
- 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.
- 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.
- Leads engineering design for complex data pipeline designs. Proficient in developing highly efficient designs and strategy for both batch and real-time data pipelines.
- 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 is data discovery techniques.
- 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.
- 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.
- 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.
- Participates in special projects and performs other duties as assigned.
Qualifications:
- Gen AI experience - finetuning SLMs , application evaluation
- Experience with AWS suite of tools such as bedrock , agent core
- Undergraduate degree or equivalent combination of training and experience. Graduate degree preferred.
- Minimum of ten years related work experience.
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.
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About Vanguard
Reviews
3.4
3 reviews
Work Life Balance
2.5
Compensation
3.2
Culture
2.8
Career
3.5
Management
3.0
45%
Recommend to a Friend
Pros
Competitive compensation package with bonuses
Good foundation for career development
Interesting programs aligned with education
Cons
Long commute requirements (2.5 hours)
Mandatory on-site presence multiple days
Pay below industry standards
Salary Ranges
1,532 data points
Junior/L3
Junior/L3 · Client Relationship Associate
529 reports
$60,018
total / year
Base
$55,076
Stock
-
Bonus
$4,942
$46,375
$78,763
Interview Experience
3 interviews
Difficulty
3.0
/ 5
Duration
14-28 weeks
Interview Process
1
Application Review
2
Recruiter/HR Phone Screen
3
Technical/Case Study Round
4
Final Round Interview
5
Offer
Common Questions
Behavioral/STAR
Technical Knowledge
Case Study
Past Experience
Culture Fit
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