Data Scientist/Machine Learning Engineer

SmartIMS Inc.

Minnetonka, MN, United States

3 days ago

Apply on www.dice.com

Prepare application

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

10 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Application Programming Interfaces (APIs) Business Analytics Applications Automation of Tests Microsoft Azure Cloud Computing Cluster Analysis Continuous Integration Data Cleansing Information Engineering Database Queries Python (Programming Language) Machine Learning

+15 more

Natural Language Processing Pattern Recognition Software Engineering Systems Integration Management of Software Versions Enterprise Software Applications Feature Engineering Large Language Models Snowflake Deep Learning Model Validation Semi-structured Data Xgboost Machine Learning Operations Software Version Control

Job description

Design, develop, and maintain anomaly detection and pattern recognition systems across large-scale healthcare and operational datasets, using techniques such as clustering, classification, time-series analysis, change-point detection, and graph-based analytics.
Develop reusable feature engineering, scoring, and analytical components that support multiple enterprise use cases rather than isolated point solutions.
Apply natural language processing, large language models, and other machine-learning techniques to unstructured and semi-structured data to surface patterns, themes, and emerging signals.
Design and contribute to production-grade machine learning pipelines, including automated data preparation, feature generation, training, validation, deployment, scoring, and monitoring.
Develop and maintain CI/CD workflows for data science solutions, including source control, automated testing, model versioning, and rollback capabilities.
Establish monitoring for production analytical systems - model performance, data quality, feature drift, model drift, and pipeline health.
Partner with engineering and technology teams to integrate models and services with enterprise applications, APIs, and downstream business processes.
Communicate analytical findings, model behavior, and limitations clearly to both technical and non-technical stakeholders.

Requirements

10 plus years experience
Strong professional experience in Data Science, Machine Learning, advanced analytics, statistical modeling, or a related discipline.
Strong hands-on programming capability in Python.
Strong SQL skills and experience working with large relational or analytical datasets.
Strong foundation in statistics, machine learning, model evaluation, and experimental design.
Experience developing real-world models using techniques such as classification, clustering, anomaly detection, predictive modeling, time-series analysis, or related approaches.
Experience with data preparation, feature engineering, target construction, validation, and model performance evaluation.
Experience developing reusable and maintainable analytical code rather than exclusively notebook-based or ad hoc analysis.
Experience helping move machine-learning or advanced-analytics solutions into production.
Understanding of model scoring, deployment, monitoring, data quality, model drift, and production lifecycle considerations.
Ability to work effectively when requirements, data, or solution approaches are incomplete or evolving.
Ability to communicate analytical methodology, findings, limitations, and business implications clearly., * Healthcare, payer, claims, payment-integrity, provider, member, clinical, financial, or other regulated-data experience.
Hands-on experience developing anomaly-detection or emerging-pattern systems.
Experience with supervised, semi-supervised, and unsupervised machine-learning techniques.
Experience with advanced modeling approaches such as gradient boosting, ensemble methods, deep learning, graph-based methods, sequence models, or representation learning.
Experience with model explainability, calibration, threshold optimization, and false-positive reduction.
Experience with Snowflake and Azure.
Experience working within containerized Data Science environments.
Familiarity with production ML and MLOps practices such as model registries, versioning, CI/CD, experiment tracking, monitoring, and lifecycle management.
Experience integrating analytical models into APIs, applications, decision systems, or enterprise workflows.
Experience working across Data Engineering, Software Engineering, MLOps, Platform, and Cloud teams.
Experience applying NLP, embeddings, or GenAI where unstructured information must be converted into structured data or incorporated into a broader analytical solution.
Experience mentoring other Data Scientists, helping establish modeling standards, or guiding analytical design decisions.