Data conversion manager leading ETL services for Catalis, a government SaaS and integrated payments provider. Scaling migration standards across court, land records, criminal justice, and jury solutions.
About the role
- Staff Data Scientist developing advanced machine-learning models for MindBridge Analytics’ enterprise datasets. Leading experimentation, production deployment, technical standards and mentorship.
Responsibilities
- Lead the design, development and evaluation of advanced machine-learning models for large-scale structured and transactional data
- Explore and apply transformers, sequence modelling, self-supervised learning and representation learning
- Design experiments, benchmarks and evaluation frameworks to compare modelling approaches and measure generalization
- Analyze complex datasets for data-quality issues, behavioural patterns, modelling opportunities and sources of bias or leakage
- Develop reusable representations and modelling approaches for downstream use cases
- Collaborate with engineering teams to train, deploy and operate models reliably at scale
- Partner with Product and domain experts to identify applications and translate technical advances into customer-facing capabilities
- Establish standards for modelling quality, reproducibility, documentation and experimentation
- Mentor data scientists and machine-learning engineers
- Communicate technical decisions, findings and trade-offs to technical and non-technical stakeholders
- Contribute to machine-learning strategy and technical roadmap
Requirements
- Substantial experience building and deploying sophisticated machine-learning systems
- Strong practical experience in several of: deep learning and modern neural-network architectures; transformer architectures, attention mechanisms or sequence models; representation learning, embeddings or self-supervised learning; structured, tabular, temporal, transactional or event-based data; predictive or generative machine-learning models; controlled experiments and rigorous model evaluation; large, noisy and heterogeneous datasets; Python and modern machine-learning frameworks such as PyTorch; CUDA and RAPIDS; production machine learning
- Experience collaborating with ML or data engineering teams
- Experience mentoring data scientists or providing technical leadership across complex projects
- PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics or another quantitative discipline, or equivalent practical experience
- Typically 7+ years of relevant industry experience in Data Science, Machine Learning or Applied Research, with demonstrated impact at a senior or staff level
- Strong understanding of machine-learning fundamentals, statistics and experimental design
- Ability to independently lead technically complex projects from problem definition through experimentation and delivery
- Strong programming and data-analysis skills
- Ability to reason clearly about ambiguous problems and make pragmatic technical decisions
- Strong written and verbal communication skills
- Track record of collaborating across Data Science, Engineering, Product and business teams
- Evidence of technical leadership through mentoring, setting standards, influencing architecture or defining modelling strategy
- Fulfill requirements necessary to obtain full background check
Benefits
- May be eligible for bonus awards
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