Senior Data Scientist, Methodologies

tilthq; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Equity

WFH reimbursement

Flexible health plans

Global gatherings

Leadership access

Growth opportunities

Job summary

tilthq is seeking a role focused on developing foundational machine learning methods for financial data, including deep learning approaches to improve credit and loss prediction. You will take experiments from question to deployment, collaborating with data scientists, engineers, and product partners to connect technical findings to customer and business outcomes.

The candidate will build and train deep learning models on financial data, run representation-learning experiments, and integrate

Qualifications

• Hands-on experience building and training deep learning models.
• Familiarity with PyTorch or TensorFlow.
• Experience applying ML to real-world problems with measurable outcomes.
• Strong communication and collaboration with technical and product partners.
• Interest in measuring model impact on customer and partner outcomes.

Responsibilities

• Build, train, and evaluate deep learning models using financial data.
• Run representation-learning experiments, including pretraining and fine-tuning, with clear hypotheses and evaluation plans.
• Integrate useful models and components into existing machine learning pipelines.
• Own projects from exploration through deployment, with guidance from team leads.
• Collaborate across data science, engineering, and product to translate results into practical solutions.
• Share methods and findings with colleagues, use agent-based tools to accelerate development, and build specialized agents for data science workflows.

Skills

Deep learning

PyTorch/TensorFlow

Applied ML

Communication skills

Model impact

Job description

Role overview

Develop foundational machine learning methods for financial data, with a focus on deep learning approaches that can improve credit and loss prediction. You’ll take experiments from initial question through model integration, working with data scientists, engineers, and product partners to connect technical findings to customer and business outcomes.

Responsibilities

• Build, train, and evaluate deep learning models using financial data.
• Run representation-learning experiments, including pretraining and fine-tuning, with clear hypotheses and evaluation plans.
• Integrate useful models and components into existing machine learning pipelines.
• Own projects from exploration through deployment, with guidance from team leads.
• Collaborate across data science, engineering, and product to translate results into practical solutions.
• Share methods and findings with colleagues, use agent-based tools to accelerate development, and build specialized agents for data science workflows.

Requirements

• Practical experience building and training deep learning models.
• Familiarity with a modern machine learning framework such as PyTorch or TensorFlow.
• Experience applying machine learning to real-world problems and taking work from an open question to a measurable outcome.
• Clear communication skills and ability to collaborate with technical and product partners.
• Interest in measuring model impact through customer and partner outcomes as well as predictive performance.

Nice to have

Experience in credit, lending, payments, banking, large language models, or agent frameworks is useful. The listing also welcomes candidates who are motivated to learn these areas.

Benefits and work setup

The team works virtually across countries and time zones, with a work-from-home reimbursement, equity, flexible health plans, twice-yearly paid global gatherings, leadership access, and growth opportunities tied to contributions.