Data Scientist

ABC Legal Services•Longmont, CO

1d•$110,000 - $130,000•Onsite

About The Position

Docketly is hiring an on-site Data Scientist in Longmont, Colorado, to build and operate the machine learning and analytics systems that support its operations. The role develops production models from initial problem framing through deployment and monitoring on AWS, builds business intelligence reporting used by leadership and operations, and supports pricing and margin decisions through financial modeling. The Data Scientist also works closely with backend engineers on data infrastructure and production issues, with methods support from ABC Legal’s data science team. The role may grow into Senior Data Scientist as the employee takes ownership of production models and the pricing roadmap. The position includes end-to-end model development and MLOps, business analytics and pricing, and cross-functional collaboration and documentation. Work includes production deployment using AWS services, automated retraining and drift monitoring, staged rollouts, BI reporting and scoring frameworks, pricing analysis, data-pipeline partnership, and documentation of methods and decisions. The schedule is Monday through Friday, 8:00 AM–5:00 PM Mountain Time, on-site in Longmont, Colorado. The salary range is $110,000–$130,000, depending on experience.

Requirements

Bachelor’s degree in a related field or equivalent practical experience.
At least 3 years of experience with applied statistics or machine learning.
Applied statistics knowledge, including probability, hypothesis testing, and evaluation of classification and regression or quantile models, such as precision, recall, calibration, quantile coverage, and drift.
At least 3 years of professional experience with SQL and a relational database.
Python experience for data analysis and modeling, including pandas and scikit-learn, and experience with a gradient-boosting framework such as XGBoost, LightGBM, or CatBoost.
Experience training and deploying at least one model to production or on a cloud ML platform such as AWS SageMaker.
Experience with Metabase or comparable BI tooling.
Clear written and verbal communication with non-technical stakeholders.

Nice To Haves

Experience with staged production rollouts, such as canary or phased releases, using holdout comparisons to demonstrate model impact before full release.
Experience with AWS Glue, S3, Lambda, EventBridge, and CloudFormation.
Comfort using AI coding tools such as Claude Code or GitHub Copilot for analysis and modeling work.
Experience diagnosing production database performance issues, including query plans, indexing, and ORM-generated query patterns in a large, high-table-count schema.
Experience applying software-engineering practices to ML code, such as automated testing with pytest for production pipelines.

Responsibilities

Build, deploy, and operate production machine-learning models through the full lifecycle, including problem framing, feature engineering, training, validation, deployment, inference infrastructure, and monitoring.
Deploy models using AWS SageMaker features such as Model Registry and real-time endpoints, and inference infrastructure such as AWS Lambda.
Build and maintain automated retraining and drift monitoring for deployed models.
Run staged production rollouts with holdout comparisons and review test designs before launch.
Use AI coding tools to accelerate analysis, modeling, and MLOps work.
Build and maintain BI reporting for executive reporting on revenue, margin, and volume, including dashboard design and data accuracy.
Analyze pricing and margins for client accounts, including ad hoc investigations for strategic accounts.
Model the financial impact of proposed pricing changes before implementation.
Build and maintain rules-based pricing and case-difficulty logic by court and county, alongside statistical and machine-learning approaches.
Build and tune large scoring and prioritization frameworks in Metabase, including weighting, percentile scenarios, and drop-rate analysis, to support operational decisions such as hearing priority and assignment.
Translate ambiguous business questions into clear, well-scoped analyses.
Partner with engineering on data pipelines using AWS Glue, Lambda, and EventBridge.
Investigate and help resolve production database performance issues, including ORM query patterns and indexing, with backend engineering.
Document methods and decisions in Confluence or an equivalent shared space so other teams can find and build on the work.