Senior Data Scientist

IMR Soft LLC; Fort Worth (TX); Hybrid; USD 140,000 - 180,000; Full time

Job summary

IMR Soft LLC seeks a Senior Data Scientist / ML Engineer to own end-to-end ML solutions in a hybrid Fort Worth setting. You will advance models from prototype to production and optimize pipelines in Cloud or Databricks environments.

The role requires a PhD and 3+ years, strong Python, ML methods, and experience delivering production-grade code, dashboards, and scalable data products for multiple business units.

Qualifications

• PhD in data science, CS, statistics, or related field.
• At least 3 years of relevant professional experience.
• Strong Python programming and production-grade code experience.
• Experience with data extraction, cleaning, transformation, and analysis.
• Knowledge of ML methods and lifecycle including deployment and monitoring.

Responsibilities

• Develop, refactor, test, and optimize production-quality Python ML code.
• Design, build, deploy, and maintain ML models and pipelines.
• Productionize ML workflows including deployment, orchestration, monitoring, and optimization.
• Analyze large-scale datasets for customer intelligence initiatives.
• Collaborate with Data Scientists, Data Engineers, Product Teams, and Stakeholders.

Skills

Python

Production-grade code

Machine Learning

Statistical methods

Cloud/Databricks environments

Education

PhD in Data Science / CS / Stats / ML

Tools

Databricks

Apache Spark

SQL

Tableau/Power BI

Azure / Cloud platforms

MLflow

CI/CD pipelines

Job description

Senior Data Scientist / Machine Learning Engineer

Location: Fort Worth, TX 76155 (Hybrid)

Interview Process: 2 Rounds – First Virtual, Second Onsite

Job Overview

Our Client is seeking an experienced Senior Data Scientist / Machine Learning Engineer to join its centralized Machine Learning team, supporting multiple business units.

The ideal candidate will have strong hands-on experience in Python programming, Machine Learning, and production-grade ML pipelines. This role focuses on taking machine learning models from prototype to production, improving existing codebases, optimizing performance, and building scalable ML solutions for the Customer Intelligence CBI productionization initiative.

Candidates should have practical experience across the complete Machine Learning Lifecycle, including model development, deployment, monitoring, orchestration, and optimization in Cloud or Databricks environments.

Required Qualifications

• PhD degree in Data Science, Computer Science, Statistics, Applied Mathematics, Machine Learning, or a related quantitative field.
• Minimum 3+ years of relevant professional experience.
• Strong hands-on experience with Python programming.
• Experience writing modular, testable, maintainable, and production-grade code.
• Practical experience in data extraction, data cleaning, transformation, and analysis.
• Strong understanding of statistical methods and Machine Learning techniques.
• Hands-on experience developing and productionizing Machine Learning pipelines.

Preferred Qualifications

• 8+ years of technical professional experience.
• Hands-on experience with Databricks and Apache Spark.
• Experience deploying Machine Learning models into production environments.
• Knowledge of ML orchestration, monitoring, and performance optimization.
• Strong experience with SQL and large-scale datasets.
• Familiarity with MLflow, CI/CD pipelines, and model-monitoring platforms.
• Experience with Azure or other Cloud platforms.
• Knowledge of parallel processing and distributed computing.
• Experience creating dashboards and visualizations using Tableau or Power BI.
• Previous experience in the Airline or Transportation industry is a plus.

Key Responsibilities

• Develop, refactor, test, and optimize production-quality Python code for Machine Learning applications.
• Design, build, deploy, and maintain Machine Learning models and pipelines.
• Productionize ML workflows, including model deployment, orchestration, monitoring, and optimization.
• Extract, clean, transform, and analyze large-scale datasets for customer intelligence initiatives.
• Apply statistical methods, Machine Learning algorithms, and feature engineering to improve model performance.
• Collaborate with Data Scientists, Data Engineers, Product Teams, and Business Stakeholders.
• Improve existing codebases, pipelines, and system integrations for reliability, scalability, maintainability, and performance.
• Troubleshoot technical issues and perform debugging and performance tuning across the ML lifecycle.
• Leverage Cloud platforms, Spark, and parallel-processing technologies to scale model development and deployment.
• Document technical solutions and communicate findings to technical and non-technical stakeholders.
• Drive quality, innovation, scalability, and measurable business impact.