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Data Scientist Predictive Modeler Jobs (NOW HIRING)

Photon

AI/ML Data Scientist | Onsite

Dallas, TX · On-site

ML/AI Data Scientist - Predictive Modeling Position Overview We are seeking an experienced ML/AI Data Scientist to design, build, train, evaluate, and deploy predictive models that solve complex ...

Select Minds LLC

Predictive Analytics Engineer

Dallas, TX · On-site

$100K - $120K/yr

Optimize existing data science workflows and improve model performance. * Ensure data quality ... Experience with statistical modeling, predictive analytics, and feature engineering. * Strong ...

Munich Re

Data Scientist, AI

Houston, TX · On-site +1

We focus on developing and maintaining predictive models that support all domains across the business. In this role, you'll collaborate closely with senior data scientists to build and support AI/ML ...

Munich Re

Data Scientist, AI

Chattanooga, TN · On-site +1

We focus on developing and maintaining predictive models that support all domains across the business. In this role, you'll collaborate closely with senior data scientists to build and support AI/ML ...

Munich Re

Data Scientist, AI

Cleveland, OH · On-site +1

We focus on developing and maintaining predictive models that support all domains across the business. In this role, you'll collaborate closely with senior data scientists to build and support AI/ML ...

Munich Re

Data Scientist, AI

Charleston, SC · On-site +1

We focus on developing and maintaining predictive models that support all domains across the business. In this role, you'll collaborate closely with senior data scientists to build and support AI/ML ...

Munich Re

Data Scientist, AI

Atlanta, GA · On-site +1

We focus on developing and maintaining predictive models that support all domains across the business. In this role, you'll collaborate closely with senior data scientists to build and support AI/ML ...

Munich Re

Data Scientist, AI

Dayton, OH · On-site +1

We focus on developing and maintaining predictive models that support all domains across the business. In this role, you'll collaborate closely with senior data scientists to build and support AI/ML ...

Munich Re

Data Scientist, AI

Ann Arbor, MI · On-site +1

We focus on developing and maintaining predictive models that support all domains across the business. In this role, you'll collaborate closely with senior data scientists to build and support AI/ML ...

Munich Re

Data Scientist, AI

Charlotte, NC · On-site +1

We focus on developing and maintaining predictive models that support all domains across the business. In this role, you'll collaborate closely with senior data scientists to build and support AI/ML ...

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Data Scientist Predictive Modeler information

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How much do data scientist predictive modeler jobs pay per year?

As of Aug 6, 2026, the average yearly pay for data scientist predictive modeler in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What does a data scientist predictive modeler do?

A Data Scientist Predictive Modeler uses statistical techniques, machine learning algorithms, and data analysis tools to identify patterns in data and make predictions about future outcomes. They gather and clean data, build predictive models, and interpret the results to help organizations make data-driven decisions. Their work is applied in areas such as customer behavior forecasting, risk assessment, and process optimization. They often collaborate with business stakeholders to translate insights into actionable strategies.

What is the difference between Data Scientist Predictive Modeler vs Data Analyst?

AspectData Scientist Predictive ModelerData Analyst
CredentialsBachelor's or Master's in Data Science, Statistics, or related fieldsBachelor's in Data Analysis, Statistics, or related fields
Work EnvironmentDevelops predictive models, uses advanced analytics, often in tech or finance industriesAnalyzes data sets, creates reports, supports decision-making across various industries
Employer UsageUsed by organizations focusing on predictive analytics and machine learningUsed by companies needing data reporting and basic analysis

While both roles work with data, a Data Scientist Predictive Modeler specializes in building predictive models and advanced analytics, whereas a Data Analyst focuses on interpreting data and generating reports. The Predictive Modeler role requires more technical skills in machine learning and statistical modeling, making it suitable for organizations aiming to forecast trends and automate decision processes.

What are some common challenges faced by data scientist predictive modelers when deploying models into production environments?

Data Scientist Predictive Modelers often encounter challenges when transitioning models from development to production. These challenges can include ensuring that models perform consistently on real-world data, handling data pipeline integration, and addressing changes in data patterns (data drift) over time. Additionally, collaborating closely with engineering teams is crucial to optimize model scalability and maintainability. Clear documentation, robust testing, and ongoing monitoring are essential practices to successfully address these challenges and ensure long-term model performance.

What are the key skills and qualifications needed to thrive as a data scientist predictive modeler?

To thrive as a Data Scientist Predictive Modeler, you need a strong background in statistics, machine learning, and data analysis, often supported by a degree in computer science, mathematics, or a related field. Proficiency in programming languages such as Python or R, experience with data visualization tools, and familiarity with machine learning libraries like scikit-learn or TensorFlow are typically required. Strong problem-solving abilities, effective communication, and a collaborative mindset are crucial soft skills that set top performers apart. These skills and qualifications are vital for building accurate predictive models, translating data insights into actionable business strategies, and effectively communicating findings to stakeholders.
More about Data Scientist Predictive Modeler jobs
What cities are hiring for Data Scientist Predictive Modeler jobs? Cities with the most Data Scientist Predictive Modeler job openings:
What job categories do people searching Data Scientist Predictive Modeler jobs look for? The top searched job categories for Data Scientist Predictive Modeler jobs are:
Data Scientist Predictive Modeler jobs near you
Infographic showing various Data Scientist Predictive Modeler job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.

AI/ML Data Scientist | Onsite

Photon

Dallas, TX • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 6 days ago

Job description


Job Description: ML/AI Data Scientist - Predictive Modeling
Position Overview
We are seeking an experienced ML/AI Data Scientist to design, build, train, evaluate, and deploy predictive models that solve complex business and operational problems. The ideal candidate combines strong statistical foundations with hands-on experience in data analysis, feature engineering, model development, and performance evaluation.
This role is especially suited to someone who can build predictive solutions from the ground up and clearly explain the "what," "why," and "how" behind their analytical and modeling decisions to both technical and non-technical stakeholders.
Key Responsibilities
  • 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, R², 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.

Compensation, Benefits and Duration
Minimum Compensation: USD 40,000
Maximum Compensation: USD 140,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full-time employees.
This position is available for independent contractors
No applications will be considered if received more than 120 days after the date of this post

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