Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A global technology leader is dedicated to creating innovative products, services, and experiences that enrich people’s lives. Driven by a culture of collaboration, creativity, and inclusion, the organization brings together diverse perspectives to develop cutting-edge consumer technologies that reach millions of users worldwide. The company is committed to leveraging advanced machine learning, data engineering, and AI capabilities to power next-generation user experiences while maintaining the highest standards of privacy, security, and quality.
The organization is seeking a highly experienced and strategic Machine Learning Data Engineer to lead machine learning data initiatives with a strong emphasis on data quality, governance, and scalability. This role focuses on transforming complex and ambiguous data challenges into structured processes, scalable policies, and high-fidelity datasets that support a broad range of machine learning applications, particularly within innovative consumer-facing technologies.
Key Responsibilities
- Lead the continuous management and improvement of machine learning dataset quality, with a particular focus on human-generated data.
- Design and implement rigorous dataset validation processes, including real-time feedback mechanisms to identify and resolve quality issues.
- Develop scalable data policies and workflows for complex consumer technology products and user-facing features.
- Translate ambiguous data quality challenges and regulatory requirements into structured, repeatable operational processes.
- Design and implement robust data evaluation frameworks to measure data quality, consistency, integrity, and impact on model performance.
- Identify key data-centric factors influencing machine learning outcomes and establish metrics to monitor performance.
- Integrate privacy, legal, regulatory, and consumer protection requirements directly into data workflows and operational processes.
- Act as a steward of data integrity across machine learning initiatives.
- Collaborate with technical and non-technical stakeholders to align data strategies with business and product objectives.
- Create analytical reports, dashboards, visualizations, and presentations to communicate insights and influence decision-making.
- Drive cross-functional initiatives that improve machine learning data quality and operational efficiency at scale.
Required Qualifications
- Bachelor’s degree in Computer Science, Data Engineering, Data Science, Mathematics, or a related field, or equivalent industry experience.
- Experience in data analysis, data engineering, and machine learning data operations.
- Experience designing and implementing data quality control processes, data curation workflows, or Human-in-the-Loop systems.
- Experience managing or coordinating cross-functional projects across multiple technical teams or organizations.
- Proven ability to lead end-to-end data strategy initiatives within the machine learning development lifecycle.
- Strong capability to drive continuous improvements through rapid iteration and data-driven decision-making.
Preferred Qualifications
- 10+ years of experience in data analysis or machine learning data operations.
- Strong experience identifying trends, generating statistical insights, and analyzing both quantitative and qualitative datasets.
- Experience operating within global data privacy frameworks such as GDPR and CCPA.
- Knowledge of legal compliance, ethical guidelines, and consumer data protection requirements for machine learning systems.
- Proven track record leading large-scale, cross-functional programs focused on machine learning data quality.
- Experience with prompt engineering, machine learning platforms, and fine-tuning Large Language Models (LLMs).
- Ability to collaborate with diverse engineering stakeholders to gather requirements and improve data processes.
- Excellent written and verbal communication skills with the ability to explain complex technical concepts to non-technical audiences.
- Exceptional analytical, problem-solving, and critical-thinking abilities.
- Adaptability and ability to thrive in fast-paced, highly ambiguous environments while learning new tools and technologies quickly.
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