About this role
You spend your days building and running the machine learning and analytics systems that power operations at Docketly, a legal-tech company in the creditors' rights industry. The work involves taking production models from initial ideas to live systems on AWS, alongside building business intelligence reporting for leadership and supporting financial pricing decisions. You will also collaborate with backend engineers on data infrastructure and resolve production issues.
The role exists to drive daily operational choices like hearing priorities and assignments through scoring frameworks, automated retraining, and drift monitoring. Someone who thrives in this environment has a strong background in applied statistics, deep experience with Python and SQL, and the ability to turn ambiguous business questions into structured analyses.
What you will do
- Manage the complete lifecycle of production machine learning models, from feature engineering and training to AWS SageMaker deployment and ongoing monitoring.
- Design and maintain the business intelligence reporting layer used by leadership to track revenue, margin, and volume.
- Analyze client accounts and model the financial impact of pricing changes before implementation.
- Build and tune large scoring and prioritization frameworks in Metabase to drive daily operational decisions.
- Partner with backend engineering to build data pipelines and troubleshoot production database performance issues.
- Document methods and decisions clearly in shared spaces so cross-functional teams can build upon the work independently.
What you need
- Hold a Bachelor's degree in a related field or possess equivalent practical experience.
- Demonstrate at least 5 years of professional experience with applied statistics or machine learning.
- Bring a minimum of 5 years of professional experience using SQL and relational databases.
- Apply strong Python skills for data analysis and modeling using pandas, scikit-learn, and gradient-boosting frameworks like CatBoost.
- Show hands-on experience training, deploying, and monitoring models on a cloud machine learning platform such as AWS SageMaker.
- Communicate clearly and effectively with non-technical stakeholders.
Nice to have
- Experience with AWS Glue, S3, Lambda, EventBridge, and CloudFormation
- Familiarity with AI coding tools such as Claude Code or GitHub Copilot
- Background diagnosing production database performance issues in large, high-table-count schemas
- Application of software engineering practices to machine learning code, including automated testing with pytest
Benefits
- Health, Dental, and Vision Insurance
- 401(k) with Company Matching
- Paid Time Off
- 7 Paid Company Holidays and 4 Floating Holidays per Year
- Life Insurance, AD&D Insurance, and Long-Term Disability
- Health Care Reimbursement, FSA, Dependent Care FSA, EAP, and Pet Insurance
Skills and tech stack
Questions candidates ask
What is the work schedule and location for this job?
This position is on-site in Longmont, Colorado, operating Monday through Friday during business hours in the Mountain Time zone.
What is the salary range for the Senior Data Scientist role?
The salary range is $135,000 to $150,000, determined by experience.
What technical stack is used for modeling and data infrastructure?
The production database runs on MySQL and Aurora, while models are deployed and monitored on AWS SageMaker using Python, pandas, scikit-learn, and CatBoost.
When does the application process close?
The application closing date is October 15, 2026, at 11:59 PM MST.
The application is on the official ABC Legal Services careers page.
Apply on company site