AI/ML Data Scientist - Dallas, TX

• Translate business problems into well-defined machine learning and predictive modeling objectives.

• Collect, clean, transform, and analyze structured and unstructured data from multiple sources.

• Perform exploratory data analysis to identify trends, relationships, anomalies, biases, and data-quality issues.

• Develop predictive models from scratch, including data preparation, feature engineering, training, validation, testing, and optimization.

• Build and apply regression models for forecasting, estimation, risk scoring, pricing, demand prediction, and related use cases.

• Build and apply classification models for segmentation, fraud detection, churn prediction, recommendation, anomaly detection, and other decision-support applications.

• Select appropriate algorithms based on the problem type, data characteristics, business requirements, interpretability needs, and operational constraints.

• Compare baseline, linear, tree-based, ensemble, and other appropriate modeling approaches.

• Tune model hyperparameters and use appropriate cross-validation strategies to improve generalization.

• Experience building and deploying AI solutions using Natural Language Processing (NLP), Computer Vision, and sequence modeling techniques for text, image, video, and time-series data.

• Strong knowledge of deep learning architectures including RNNs, LSTMs, GRUs, CNNs, and Transformer-based models, with hands-on experience using TensorFlow or PyTorch.

• Ability to evaluate, optimize, and explain AI model performance, including model accuracy, robustness, bias detection, feature interpretation, and production monitoring.

• Evaluate model performance using relevant metrics such as RMSE, MAE, R , accuracy, precision, recall, F1 score, ROC-AUC, PR-AUC, log loss, calibration, and lift.

• Analyze model errors and identify opportunities for improving data quality, features, sampling strategies, and model assumptions.

• Assess model robustness, explainability, fairness, stability, and sensitivity to changing data patterns.

• Clearly communicate the rationale behind model selection, including why a particular model was chosen over alternatives.

• Explain technical results, assumptions, limitations, and trade-offs to product managers, business leaders, and other stakeholders.

• Document analytical methods, data sources, assumptions, experiments, model decisions, and results.

• Collaborate with data engineers, software engineers, product teams, domain experts, and business stakeholders to operationalize models.

• Support model deployment, monitoring, retraining, and continuous improvement in production environments.

• Stay current with developments in machine learning, statistical modeling, AI techniques, and responsible AI practices.

Required Qualifications

• Bachelor s or master s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative discipline.

• 3+ years of professional experience in data science, machine learning, predictive analytics, or a closely related field.

• Strong understanding of statistical analysis, probability, experimental design, and machine learning fundamentals.

• Demonstrated experience building predictive models from raw data through final evaluation.

• Deep practical expertise in regression and classification algorithms, including:

- Linear and polynomial regression

- Logistic regression

- Regularization methods such as Ridge, Lasso, and Elastic Net

- Decision trees

- Random forests

- Gradient boosting methods

- Support vector machines

- k-nearest neighbors

- Naive Bayes

- Ensemble modeling techniques

- CNN

- Computervision

- RNN

- NLP

• Strong knowledge of supervised learning workflows, including data splitting, cross-validation, feature selection, feature engineering, model tuning, and evaluation.

• Proficiency in Python and common data science libraries such as pandas, NumPy, scikit-learn, SciPy, and matplotlib or Seaborn.

• Strong SQL skills and experience querying, joining, aggregating, and analyzing data from relational databases.

• Experience working with missing data, outliers, imbalanced classes, categorical variables, high-cardinality features, and data leakage risks.

• Ability to select and justify appropriate evaluation metrics based on business objectives and model use cases.

• Experience explaining model behavior using techniques such as feature importance, partial dependence, SHAP, coefficients, permutation importance, or related methods.

• Excellent written and verbal communication skills.

• Ability to present complex analytical concepts clearly to audiences with varying levels of technical expertise.

Preferred Qualifications

• Experience deploying machine learning models through APIs, batch pipelines, or cloud-based platforms.

• Familiarity with MLflow, Kubeflow, Airflow, Docker, Git, CI/CD, or similar tools.

• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.

• Knowledge of time-series forecasting, survival analysis, recommender systems, or anomaly detection.

• Experience with deep learning frameworks such as PyTorch or TensorFlow.

• Familiarity with model monitoring, data drift, concept drift, model retraining, and performance degradation.

• Experience working with distributed data-processing tools such as Spark.

• Knowledge of responsible AI, model governance, fairness, privacy, and regulatory requirements.

• Experience working in an Agile or cross-functional product development environment.

Core Competencies

Analytical Thinking

Ability to break down ambiguous problems, identify relevant data, test assumptions, and develop rigorous analytical solutions.

Model Selection and Justification

Ability to explain why a specific model is appropriate based on accuracy, interpretability, scalability, latency, data volume, feature relationships, regulatory requirements, and business impact.

Statistical and Technical Expertise

Strong understanding of statistical concepts and practical machine learning methods, with the ability to distinguish correlation from causation and identify modeling limitations.

Data Understanding

Ability to assess data quality, determine whether variables are meaningful, identify bias and leakage, and understand how data-generating processes affect model results.

Communication

Ability to communicate model assumptions, results, trade-offs, uncertainty, and limitations in clear and accessible language.

Business Orientation

Ability to connect technical modeling outcomes to measurable business goals, operational decisions, customer outcomes, or financial impact.

Collaboration

Ability to work effectively with engineering, product, operations, and leadership teams throughout the model lifecycle.

Expected Modeling Approach

Successful candidates should be able to demonstrate a structured approach that includes:

1. Defining the business problem and prediction target.

1. Establishing a simple and interpretable baseline.

1. Understanding the data-generating process and identifying potential biases.

1. Performing exploratory data analysis.

1. Preparing the data and engineering meaningful features.

1. Selecting candidate models based on the problem and constraints.

1. Training and validating models using appropriate methodology.

1. Comparing models using business-relevant metrics.

1. Explaining model behavior and identifying limitations.

1. Selecting the final model based on accuracy, interpretability, reliability, and operational fit.

1. Documenting the decision-making process.

1. Monitoring and improving the model after deployment.

Deliverables and Success Measures

• High-quality exploratory analyses that produce actionable insights.

• Reliable regression and classification models aligned with business objectives.

• Clearly documented modeling decisions and assumptions.

• Reproducible data preparation and model-training workflows.

• Measurable improvements in forecasting accuracy, decision quality, efficiency, revenue, risk reduction, or customer outcomes.

• Models that are appropriately interpretable, robust, maintainable, and production-ready.

• Clear communication of model performance, uncertainty, trade-offs, and limitations.

• Effective collaboration with stakeholders throughout the analytics and model development lifecycle.