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Xgboost Jobs (NOW HIRING)

Advance Auto Parts

Data Scientist

Raleigh, NC · On-site +1

Apply advanced techniques such as structural time series modeling and boosting algorithms (e.g., XGBoost, LightGBM). * Train and Tune Models: Develop and tune machine learning models using Python ...

Xforia, Inc.

Data Scientist

... XGBoost) o Understanding of deep learning frameworks (TensorFlow/PyTorch) o Distributed computing (Spark/Scala) o Orchestration tools such as Apache Airflow o CI/CD pipelines o Agile o Git and ...

GARGI TECHNOLOGIES INC

Data Analyst

Pennsburg, PA · On-site

Experience with Machine Learning frameworks such as Scikit-learn, TensorFlow, PyTorch, or XGBoost. * Solid understanding of statistics, probability, hypothesis testing, and predictive modeling.

Experian

$81K - $107K/yr

Experience with regression, XGBoost, and other core ML algorithms. * Hands-on experience across the full model lifecycle: data ingestion, EDA, modeling, validation, and deployment. * Experience ...

OKX

Sr Data Scientist, Risk Strategy

San Jose, CA · On-site

$223K - $268K/yr

Deep understanding of modern machine learning techniques / algorithms including GBM, XGBoost, LGBM, etc. Advanced programming skills of statistical / analytical software (SQL, R, Python,etc.

OKX

Sr Data Scientist, Risk Strategy

San Jose, CA · Hybrid

$223K - $268K/yr

Deep understanding of modern machine learning techniques / algorithms including GBM, XGBoost, LGBM, etc. Advanced programming skills of statistical / analytical software (SQL, R, Python,etc.

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Broad familiarity with the Python ecosystem and common libraries including Scikit-Learn, XGBoost, PyTorch, Keras, Tensorflow, Pandas, and common ML cloud services. * Familiarity with CNNs, RNN, LSTMs ...

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How much do xgboost jobs pay per hour?

As of Jul 10, 2026, the average hourly pay for xgboost in the United States is $36.26, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $39.66 per hour, depending on experience, location, and employer.

Is XGBoost still popular?

XGBoost remains a popular machine learning algorithm used in data science and AI roles due to its high performance and efficiency in structured data tasks. It is widely valued for its speed, scalability, and effectiveness in competitions like Kaggle, making it a common skill for data analysts and machine learning engineers.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer specializing in XGBoost, and why are they important?

To thrive as a Machine Learning Engineer specializing in XGBoost, you need a strong background in statistics, data analysis, and programming (especially Python), often supported by a degree in computer science or a related field. Proficiency with XGBoost, data preprocessing libraries (like pandas and NumPy), and experience with machine learning platforms such as scikit-learn or TensorFlow are typically required. Analytical thinking, problem-solving, and effective communication are essential soft skills for interpreting results and collaborating with stakeholders. These skills ensure accurate model development, efficient implementation, and impactful business outcomes from machine learning projects.

What is XGBoost?

XGBoost stands for eXtreme Gradient Boosting and is an open-source machine learning library that provides an efficient and scalable implementation of gradient boosting algorithms. It is commonly used for supervised learning tasks, such as classification and regression, due to its high performance, speed, and ability to handle missing values. XGBoost supports parallel processing, regularization to prevent overfitting, and can be used with various programming languages like Python, R, and Julia. Its popularity stems from its success in many machine learning competitions and real-world applications.

What types of projects or datasets do professionals commonly work with when using XGBoost in a machine learning role?

Professionals using XGBoost often tackle projects involving structured data, such as customer analytics, credit scoring, fraud detection, and sales forecasting. XGBoost is particularly valued for its speed and accuracy with large tabular datasets, making it a popular choice in finance, healthcare, and e-commerce. On a daily basis, you may collaborate with data engineers to preprocess data, work with data scientists to tune hyperparameters, and communicate findings to business stakeholders. The role typically involves iterating on feature engineering, model evaluation, and integrating models into production pipelines.

Is 40 too late for data science?

Age is generally not a barrier to entering data science roles, including positions involving XGBoost and other machine learning tools. Many professionals successfully transition into data science later in their careers by acquiring relevant skills, certifications, and experience. Employers value skills and problem-solving ability over age, making it possible to start or switch into data science at 40 or older.

What is the difference between Xgboost vs Data Scientist?

AspectXgboostData Scientist
Primary RoleDeveloping and tuning machine learning models, especially gradient boosting algorithmsAnalyzing data, building models, and deriving insights across various techniques
Required SkillsProgramming (Python, R), machine learning, data preprocessingStatistics, programming, data visualization, machine learning
Work EnvironmentData science teams, machine learning projects, software developmentResearch, data analysis, cross-functional collaboration

While Xgboost is a specific machine learning algorithm used within data science projects, a Data Scientist encompasses a broader role involving data analysis, modeling, and insights. Xgboost is a tool often employed by Data Scientists to improve predictive performance, but the Data Scientist's responsibilities extend beyond just implementing algorithms.

Is data science still worth it in 2026?

Data science remains a valuable field in 2026, with roles involving machine learning models like XGBoost, data analysis, and predictive modeling. Skills in programming, statistics, and tools such as Python and SQL are essential for success in this evolving industry.

Is ML a high paying job?

Machine learning roles, including positions involving XGBoost, are generally well-paid due to high demand for data science and AI skills. Salaries vary based on experience, location, and industry, but professionals with expertise in machine learning tools and algorithms often earn above average wages in the tech sector.
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What job categories do people searching Xgboost jobs look for? The top searched job categories for Xgboost jobs are:
Xgboost jobs near you
Infographic showing various Xgboost job openings in the United States as of July 2026, with employment types broken down into 96% Full Time, and 4% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $75,411 per year, or $36.3 per hour.
Data Scientist

Data Scientist

Advance Auto Parts

Raleigh, NC • On-site, Remote

Full-time

Posted 13 days ago

Job description

Job Description

Role Summary

We are seeking an experienced Data Scientist with strong expertise in Data Science, machine learning engineering with hands on experience in designing and deploying ML solutions in production. This role focuses on building scalable ML solutions, productionizing models, and enabling robust ML platforms for enterprise-grade deployments.

This role is a hybrid work model (4 days in office, 1 day work from home) based out of our corporate headquarters located in Raleigh, NC

Key Responsibilities

  • Build ML Models: Design and implement predictive and prescriptive models for regression, classification, and optimization problems.Apply advanced techniques such as structural time series modeling and boosting algorithms (e.g., XGBoost, LightGBM).
  • Train and Tune Models: Develop and tune machine learning models using Python, PySpark, TensorFlow, and PyTorch.
  • Collaboration & Communication: Work closely with stakeholders to understand business challenges and translate them into data science solutions and work in the end-to-end solutioning. Collaborate with cross-functional teams to ensure successful integration of models into business processes.
  • Monitoring & Visualization: Rapidly prototype and test hypotheses to validate model approaches. Build automated workflows for model monitoring and performance evaluation. Create dashboards using tools like Databricks and Palantir to visualize key model metrics like model drift, Shapley values etc.
  • Productionize ML: Build repeatable paths from experimentation to deployment (batch, streaming, and low-latency endpoints), including feature engineering, training, evaluation,
  • Own ML Platform: Stand up and operate core platform components-model registry, feature store, experiment tracking, artifact stores, and standardized CI/CD for ML.
  • Pipeline Engineering: Author robust data/ML pipelines (orchestrated with Step Functions / Airflow / Argo) that train, validate, and release models on schedules or events.
  • Observability & Quality: Implement end-to-end monitoring, data validation, model/drift checks, and alerting SLA/SLOs.
  • Governance & Risk: Enforce model/version lineage, reproducibility, approvals, rollback plans, auditability, and cost controls aligned to enterprise policies.
  • Partner & Mentor: Collaborate with on-shore/off-shore teams; coach data scientists on packaging, testing, and performance; contribute to standards and reviews.
  • Hands-on Delivery: Prototype new patterns; troubleshoot production issues across data, model, and infrastructure layers.

Required Qualifications

  • Education: Bachelor's degree in Computer Science, Information Technology, Data Science, or Mathematics, Statistics or related field. MS Preferred.
  • Programming: 5+ years experience with Python (pandas, PySpark, scikit-learn; familiarity with PyTorch/TensorFlow helpful), bash, experience with Docker.
  • ML Experimentation: Design and implement predictive and prescriptive models for regression, classification, and optimization problems. Apply advanced techniques such as structural time series modeling and boosting algorithms (e.g., XGBoost, LightGBM).
  • ML Tooling: 5+ years experience with SageMaker (training, processing, pipelines, model registry, endpoints) or equivalents (Kubeflow, MLflow/Feast, Vertex, Databricks ML).
  • Pipelines & Orchestration: 5+ years' experience with Databricks DABS or Airflow or Step Functions, e-driven designs with EventBridge/SQS/Kinesis.
  • Cloud Foundations: 3+ years experience with AWS/Azure/GCP on various services like ECR/ECS, Lambda, API Gateway, S3, Glue/Athena/EMR, RDS/Aurora (PostgreSQL/MySQL), DynamoDB, CloudWatch, IAM, VPC, WAF. GCP experience is preferred.
  • Snowflake Foundations: Warehouses, databases, schemas, stages, Snowflake SQL, RBAC, UDF, Snowpark.
  • CI/CD: 3+ years hands-on experience with CodeBuild/Code Pipeline or GitHub Actions/GitLab; blue/green, canary, and shadow deployments for models and services.
  • Feature Pipelines: Proven experience with batch/stream pipelines, schema management, partitioning, performance tuning; parquet/iceberg best practices.
  • Testing & Monitoring: Unit/integration tests for data and models, contract tests for features, reproducible training; data drift/performance monitoring.
  • Operational Mindset: Incident response for model services, SLOs, dashboards, runbooks; strong debugging across data, model, and infra layers.
  • Soft Skills: Clear communication, collaborative mindset, and a bias to automate & document.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age national origin, religion, sexual orientation, gender identity, status as a veteran and basis of disability or any other federal, state or local protected class. We comply with all applicable federal, state, and local laws.

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https://jobs.advanceautoparts.com/us/en/disclosures
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About Advance Auto Parts

Sourced by ZipRecruiter

At Advance Auto Parts we have a passion for YES. Each day we are motivated by a passion to help our Customers. We have a commitment to advance the lives of our fellow Team Members, Customers, and the Communities where we live and work.

Industry

Motor vehicle and motor vehicle parts wholesalers, retail, internet and it and elementary and secondary schools

Company size

10,000+ Employees

Headquarters location

Raleigh, NC, US

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