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Xgboost Jobs (NOW HIRING)

Salesforce

DS with GEN AI

Murphy, TX · On-site

$14.25 - $18.50/hr

... XGBoost, Linear Regression, Clustering, Decicion Tree, KNN, SVN, etc. • LangChain & LangGraph: Hands-on experience building, deploying, and maintaining applications using LangChain and LangGraph ...

Salesforce

DS with GEN AI

Plano, TX · On-site

... XGBoost, Linear Regression, Clustering, Decicion Tree, KNN, SVN, etc. • LangChain & LangGraph: Hands-on experience building, deploying, and maintaining applications using LangChain and LangGraph ...

Lorven Technologies

ML Ops Engineer

Concord, CA · On-site

Strong proficiency in Java and Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch). Experience with cloud platforms and containerization (Docker, Kubernetes). Familiarity ...

Steampunk

Data Scientist

Mclean, VA

$125K - $160K/yr

Develop machine learning models using frameworks such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar libraries. * Perform feature engineering, dataset preparation, and model optimization ...

Advance Auto Parts, Inc.

Data Scientist

Raleigh, NC · On-site

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 ...

GDIT

Senior Data Scientist

Concord, NC

Design, train, and validate supervised, unsupervised, and deep learning models using open-source libraries (PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM) to support forecasting ...

Steampunk

Data Scientist

Mclean, VA · On-site

$125K - $160K/yr

Develop machine learning models using frameworks such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar libraries. * Perform feature engineering, dataset preparation, and model optimization ...

Steampunk

Data Scientist

Mclean, VA · On-site

$125K - $160K/yr

Develop machine learning models using frameworks such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar libraries. * Perform feature engineering, dataset preparation, and model optimization ...

Pattern AI

Machine Learning Engineer

San Mateo, CA

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 ...

Tror AI for everyone

MLOPS Engineer

Concord, CA · On-site

Strong proficiency in Java and Python , SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch). * Experience with cloud platforms and containerization (Docker, Kubernetes). * Hands ...

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Xgboost information

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How much do xgboost jobs pay per hour?

As of Jul 9, 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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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.
Lead Data Scientist - Propensity & Segmentation (Telecom)

Lead Data Scientist - Propensity & Segmentation (Telecom)

Emergere Technologies

Irving, TX • On-site

Other

Posted 29 days ago

Job description

Job Title: Lead Data Scientist – Propensity & Segmentation (Telecom) ) – 2 Positions

Location: Irving TX

 

ROLE SUMMARY

We build the propensity models and customer segmentation frameworks that drive how we target, acquire, and retain millions of households. This is a 100% hands-on role for a seasoned Data Scientist who loves digging into data and owning execution from end to end. We are looking for someone who can write highly optimized, large-scale SQL feature queries, apply rigorous traditional machine learning methods (avoiding rookie pitfalls like data leakage or uncalibrated models), and turn raw data into high-value targeting lists for marketing.

If you are a practitioner who thrives on optimizing data pipelines, mastering telecom data structures, and applying core data science principles to large-scale datasets, this role is for you.

WHAT YOU WILL DO

  • Hands-on Feature Engineering: Write, debug, and optimize complex SQL queries on cloud data warehouses. You will build clean feature sets from raw, massive source tables spanning customer billing, network performance, competitive footprint, and geographic data.
  • Predictive & Behavioral Modeling: Build, calibrate, and maintain propensity and "take rate" models utilizing gradient boosted trees (e.g., XGBoost, LightGBM) to optimize marketing spend.
  • Customer Archetypes: Develop unsupervised clustering and segmentation frameworks to group customers and addresses, enabling hyper-personalized marketing workflows.
  • Enforce Core DS Rigor: Engineer features utilizing strict time-series windows to rigorously protect against data leakage, lookahead bias, and overfitting.
  • Model Explainability & Performance: Evaluate and explain model mechanics using SHAP and feature importance. Monitor models in production to detect and remediate data and concept drift.
  • Experimental Design: Collaborate with marketing teams to design A/B tests and randomized control trials (RCTs) to measure true incremental lift and isolate campaign performance from organic consumer behavior.
  • Deliver Actionable Outcomes: Cleanly package outputs into business-ready deliverables, including feature dictionaries, performance tier charts, and scored target lists.

TELECOM & GEOSPATIAL REQUIREMENTS (MUST HAVE)

  • Telecom Domain Expertise: 3+ years specifically navigating telecom, broadband, wireless, or subscription-based data structures (e.g., understanding ARPU, churn cycles).
  • Geospatial Literacy: Practical experience using spatial SQL functions (e.g., BigQuery GIS, PostGIS, H3/S2 spatial indexing) to join and analyze location-based data like lat/long coordinates, wire centers, or census tracts.

REQUIRED SQL & BIG DATA SKILLS

  • Advanced Cloud SQL & Tuning: Expert-level SQL proficiency on cloud data warehouses (BigQuery, Snowflake, or Redshift). You must know how to diagnose and fix poorly performing queries, optimize complex window functions, and handle heavy aggregations on tens of millions of rows efficiently.
  • Memory Optimization: Practical experience handling datasets that exceed local memory constraints using batching, sampling, or large-scale data frameworks (e.g., PySpark, Dask, or warehouse-native tools like BigQuery ML/Snowpark).

REQUIRED MACHINE LEARNING & EXPERIENCE

  • Experience: 5+ years of professional experience as an applied Data Scientist building and deploying supervised and unsupervised machine learning models.
  • Core DS Fundamentals: Deep understanding of traditional ML theory, including class imbalance mitigation, feature selection, probability calibration, and experimental design.
  • Business-Centric Evaluation: Ability to evaluate models beyond standard AUC/ROC, focusing on lift charts, precision-recall curves, tier separation, and financial ROI.
  • Python Ecosystem: Advanced proficiency in Python, specifically utilizing the traditional data science stack (pandas, NumPy, scikit-learn, XGBoost, LightGBM) within notebook and script-based workflows.

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