Machine Learning Engineer or Data Scientist

Donato Technologies Inc; Dallas (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

Donato Technologies Inc is seeking a senior-level Machine Learning Engineer or Data Scientist to independently deliver complex classification models from data exploration through production deployment. You will work with Python, feature engineering, and scalable pipelines in a data-rich environment.

The role emphasizes hands-on model development, rigorous evaluation, documentation, and collaboration with engineering teams to integrate models into production workflows with measurable

Qualifications

• Master’s or PhD in a quantitative or technical field such as computer science, statistics, mathematics, engineering, data science, or a related discipline.
• 5–10 years of hands‑on data science or machine learning engineering experience delivering models, pipelines, or analytics solutions in highly technical environments.
• Strong Python development experience, including experience with common data science and machine learning libraries such as pandas, NumPy, scikit-learn, XGBoost, or similar frameworks.
• Deep understanding of supervised machine learning classification approaches, including feature engineering, model training, hyperparameter tuning, validation, threshold selection, and model performance evaluation.
• Experience working with large structured datasets, performing data cleansing, transformation, exploratory analysis, and building repeatable data preparation workflows.
• Ability to evaluate classification models using metrics such as precision, recall, F1 score, ROC‑AUC, confusion matrices, and false positive or false negative analysis.
• Experience packaging, versioning, testing, and integrating machine learning models or scoring logic into production or near‑production application workflows.
• Strong technical communication and documentation skills, including the ability to explain model assumptions, feature logic, validation results, implementation details, and operational tradeoffs.
• Experience using generative AI tools, such as "Microsoft Copilot", and applying prompt engineering techniques.

Responsibilities

• Design, build, train, validate, and tune supervised machine learning classification models using structured, behavioral, and transactional data.
• Perform hands-on data exploration, cleansing, transformation, and feature engineering to create reliable inputs for model development.
• Develop repeatable Python-based workflows, scripts, and pipelines for dataset preparation, model training, evaluation, and scoring.
• Evaluate model performance using appropriate classification metrics, analyze false positives and false negatives, and iterate on model quality based on results.
• Package and deploy machine learning models into scalable production or near-production environments in partnership with application and platform engineering teams.
• Troubleshoot data quality, pipeline, model performance, and integration issues across development, testing, and deployment phases.
• Create clear technical documentation covering data assumptions, feature logic, model design, validation results, implementation details, and operational considerations.
• Collaborate with engineers, analysts, and stakeholders to ensure delivered models are technically sound, maintainable, explainable, and aligned to measurable classification outcomes.

Skills

Model Development

Python

Core Java

Education

Master's or PhD in quantitative field

Tools

Pandas

NumPy

scikit-learn

XGBoost

Job description

Position Summary:

• We are looking for a senior-level Machine Learning Engineer or Data Scientist who can independently deliver complex classification models from data exploration through production implementation. The ideal candidate has strong hands-on experience with Python, feature engineering, model training, evaluation, tuning, validation, and deployment of machine learning solutions in technical environments. This person should be comfortable working directly with large datasets, building repeatable data pipelines, writing clean and maintainable code, troubleshooting model and data issues, documenting technical decisions, and partnering with engineering teams to integrate models into scalable production workflows.
• Design, build, train, validate, and tune supervised machine learning classification models using structured, behavioral, and transactional data.
• Perform hands-on data exploration, cleansing, transformation, and feature engineering to create reliable inputs for model development.
• Develop repeatable Python-based workflows, scripts, and pipelines for dataset preparation, model training, evaluation, and scoring.
• Evaluate model performance using appropriate classification metrics, analyze false positives and false negatives, and iterate on model quality based on results.
• Package and deploy machine learning models into scalable production or near-production environments in partnership with application and platform engineering teams.
• Troubleshoot data quality, pipeline, model performance, and integration issues across development, testing, and deployment phases.
• Create clear technical documentation covering data assumptions, feature logic, model design, validation results, implementation details, and operational considerations.
• Collaborate with engineers, analysts, and stakeholders to ensure delivered models are technically sound, maintainable, explainable, and aligned to measurable classification outcomes.

Primary Skill-AI —

Model Development

Secondary Skill-Python

Tertiary Skill-Core Java

Required Qualifications:

• Master’s or PhD in a quantitative or technical field such as computer science, statistics, mathematics, engineering, data science, or a related discipline.
• 5–10 years of hands‑on data science or machine learning engineering experience delivering models, pipelines, or analytics solutions in highly technical environments.
• Strong Python development experience, including experience with common data science and machine learning libraries such as pandas, NumPy, scikit-learn, XGBoost, or similar frameworks.
• Deep understanding of supervised machine learning classification approaches, including feature engineering, model training, hyperparameter tuning, validation, threshold selection, and model performance evaluation.
• Experience working with large structured datasets, performing data cleansing, transformation, exploratory analysis, and building repeatable data preparation workflows.
• Ability to evaluate classification models using metrics such as precision, recall, F1 score, ROC‑AUC, confusion matrices, and false positive or false negative analysis.
• Experience packaging, versioning, testing, and integrating machine learning models or scoring logic into production or near‑production application workflows.
• Strong technical communication and documentation skills, including the ability to explain model assumptions, feature logic, validation results, implementation details, and operational tradeoffs.
• Experience using generative AI tools, such as "Microsoft Copilot", and applying prompt engineering techniques.

Desired Qualifications:

• Experience developing classification models for fraud, risk, cybersecurity, authentication, customer behavior, anomaly detection, or other high‑volume decisioning use cases.
• Familiarity with MLOps practices, including model packaging, version control, automated testing, CI/CD pipelines, model monitoring, and reproducible deployment workflows.
• Experience designing, deploying, or supporting machine learning models where inference must be delivered in real time or near real time, including low‑latency scoring, performance optimization, and integration with production decisioning workflows.
• Experience with distributed data processing, cloud platforms, or large‑scale analytics environments such as Spark, Databricks, Hadoop, AWS, Azure, or similar technologies.
• Working knowledge of SQL and ability to extract, join, profile, and validate data from relational or analytical data stores.
• Experience creating dashboards, reports, or visualizations to communicate model performance, operational trends, feature behavior, and classification outcomes.
• Understanding explainable AI, model governance, responsible AI practices, bias evaluation, auditability, and risk controls for production machine learning systems.
• Prior experience working in Agile delivery environments with engineering teams, product partners, analysts, and operational stakeholders.