Machine Learning Scientist

Position Overview

We are seeking a highly analytical and innovative Machine Learning Scientist to research, develop, and deploy advanced machine learning models and AI-driven solutions that solve complex business problems. This role combines scientific research, data science, and machine learning engineering to design predictive models, optimize algorithms, and drive data-informed decision-making across the organization.

The ideal candidate has strong expertise in machine learning, statistics, data modeling, experimentation, and applied AI. They should be comfortable working with large datasets, developing production-ready models, conducting research, and collaborating with cross-functional teams to transform business challenges into scalable AI solutions.

Location Requirement

To support collaboration, client engagement, and strategic initiatives, candidates must currently reside in one of the following metropolitan areas in the United States:

• Dallas

• Houston

• Austin

• Atlanta

• Jacksonville

• Miami

• Nashville

• Charlotte

• Phoenix

Candidates residing outside of these locations will not be considered for this position.

Key Responsibilities

Machine Learning Research & Development

• Research, design, and develop machine learning models for business and product applications

• Evaluate and implement supervised, unsupervised, reinforcement learning, and deep learning techniques

• Develop predictive, classification, recommendation, forecasting, and optimization models

• Conduct model experimentation, validation, and performance analysis

• Stay current with emerging machine learning research, methodologies, and industry advancements

• Translate research findings into practical business solutions

Data Analysis & Feature Engineering

• Analyze large structured and unstructured datasets

• Design and implement feature engineering strategies

• Identify patterns, trends, anomalies, and predictive signals within data

• Build scalable data preparation and transformation pipelines

• Collaborate with Data Engineers to ensure data quality and accessibility

• Develop datasets for training, validation, and testing purposes

Statistical Modeling & Experimentation

• Apply statistical techniques to solve complex analytical problems

• Design and execute experiments, A/B tests, and hypothesis testing frameworks

• Evaluate model effectiveness using appropriate metrics and methodologies

• Conduct causal inference and advanced analytical studies when required

• Generate actionable insights from model outputs and experimental results

• Communicate findings to technical and non-technical stakeholders

AI & Advanced Modeling

• Develop and optimize deep learning, neural network, and advanced AI models

• Explore Generative AI, Natural Language Processing (NLP), and Computer Vision applications when applicable

• Implement model tuning and optimization techniques

• Evaluate emerging AI technologies for business opportunities

• Support AI innovation and research initiatives

• Collaborate with AI Engineering teams on model deployment and integration

Model Deployment & MLOps Collaboration

• Partner with Machine Learning Engineers and Software Engineers to deploy models into production

• Develop scalable and maintainable model architectures

• Support model monitoring, retraining, and lifecycle management

• Collaborate on MLOps pipelines and automation frameworks

• Ensure model reliability, reproducibility, and performance in production environments

• Contribute to AI infrastructure planning and optimization

Business Impact & Stakeholder Collaboration

• Work closely with Product, Operations, Marketing, Sales, and Leadership teams

• Translate business challenges into machine learning solutions

• Present technical findings and recommendations to stakeholders

• Support strategic decision-making through predictive analytics and data science insights

• Measure and communicate the business impact of machine learning initiatives

• Contribute to AI and data science roadmaps

Responsible AI, Governance & Compliance

• Ensure ethical and responsible use of machine learning technologies

• Identify and mitigate model bias and fairness concerns

• Support AI governance and model documentation standards

• Ensure compliance with privacy, security, and regulatory requirements

• Maintain transparency and explainability in model development

• Participate in model risk management and validation activities

Qualifications

Required

• Master's degree or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field

• 3+ years of experience in machine learning, data science, AI research, or related disciplines

• Strong proficiency in Python and machine learning frameworks

• Experience with machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, XGBoost, or similar tools

• Strong knowledge of statistics, probability, and predictive modeling techniques

• Experience working with large datasets and data analysis tools

• Familiarity with SQL and data querying technologies

• Strong analytical thinking and problem-solving abilities

• Excellent communication and presentation skills

• Must currently reside in one of the approved locations listed above

Preferred (Nice-to-Have)

• Ph.D. in Machine Learning, Artificial Intelligence, Statistics, or related field

• Experience with Deep Learning, NLP, Computer Vision, or Generative AI

• Experience with Large Language Models (LLMs) and AI research

• Familiarity with cloud platforms such as AWS, Azure, or GCP

• Experience with MLOps, model deployment, and production AI systems

• Knowledge of Spark, Databricks, Hadoop, or distributed computing frameworks

• Experience publishing research papers, patents, or technical publications

• Experience with reinforcement learning or advanced optimization techniques

• Familiarity with AI governance, model explainability, and responsible AI frameworks

• Experience working in highly regulated industries such as finance, healthcare, or insurance

Key Performance Indicators (KPIs)

Research & Model Development

• Number of machine learning models developed and deployed

• Model accuracy, precision, recall, F1 score, and business performance metrics

• Improvement in predictive performance over baseline models

• Research initiatives completed and successfully implemented

Business Impact

• Revenue growth or cost savings generated through machine learning initiatives

• Operational efficiency improvements

• Stakeholder satisfaction with analytical insights and recommendations

• Adoption and utilization of machine learning solutions

Model Quality & Reliability

• Model stability and production performance

• Reduction in prediction errors and false positives/negatives

• Monitoring and retraining effectiveness

• Compliance with model governance standards

Innovation & Research

• New methodologies, techniques, or technologies evaluated

• Proof-of-concept projects completed

• Contributions to AI and data science strategy

• Knowledge sharing and technical leadership activities

Collaboration & Communication

• Cross-functional project success rate

• Timeliness and quality of stakeholder reporting

• Effectiveness of technical presentations and recommendations

• Team collaboration and mentorship contributions

Reporting To

• Director of Data Science

• Head of Machine Learning

• AI Research Lead

• Director of Artificial Intelligence

• Chief Data Officer (CDO)

• Chief Technology Officer (CTO)

Employment Type & Work Setup

• Full-Time

• Remote (Candidates must reside in approved locations)

• Hybrid opportunities may be available based on business requirements

• Agile and research-driven work environment

• Participation in AI innovation, experimentation, and advanced analytics initiatives

Work Environment & Conditions

• Fast-paced, data-driven, and innovation-focused organization

• Collaborative environment with AI, Engineering, Product, and Business teams

• Access to large-scale datasets, cloud infrastructure, and modern AI tools

• Opportunity to work on cutting-edge machine learning and AI initiatives

• Strong focus on research, experimentation, business impact, and continuous learning

• Career growth opportunities within Data Science, AI Research, and Machine Learning Leadership