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Telecom Data Analyst Jobs (NOW HIRING)

Crox Consulting Inc

Telecom Expense Analyst

New York, NY · On-site

$65 - $68/hr

Analyze report for data integrity. • Establish/Optimize process to Review Cost Benefit Analyses ... Telecom Services & Expense Management • Experience with project-based financial management ...

Averna

AI Savvy Data Analyst

Denver, CO

... semiconductor, telecom and transportation industries. Their solutions include prototyping ... Data Analyst - AI focused in Hardware Manufacturing, Quality & Reliability Role Summary This role ...

Averna

AI Savvy Data Analyst

Denver, CO · On-site +1

... semiconductor, telecom and transportation industries. Their solutions include prototyping ... Data Analyst - AI focused in Hardware Manufacturing, Quality & Reliability Role Summary This role ...

Averna

AI Savvy Data Analyst

Denver, CO

... telecom and transportation industries.  Their solutions include prototyping & consulting ... Data Analyst - AI focused in Hardware Manufacturing, Quality & Reliability Role Summary This role ...

Abode Tech Zone

Telecom Business Analyst

Dallas, TX · On-site

Telecom Business Analyst Location : Dallas, TX Job Type: 6+ months Contract Overview: As a Senior ... Data Analysis: Utilize advanced data analytics tools to interpret sales data, customer insights ...

Bloom Energy

Equipment Maintenance Data Analyst

Fremont, CA · On-site

$72K - $103K/yr

... telecom facilities, utilities, and other critical infrastructure customers around the world. Our ... We are seeking a detail-oriented and analytical Equipment Maintenance Data Analyst to collect ...

HCL

Business Data Analyst

Bellevue, WA · On-site

... of a telecom customer. * Requirement elicitation: Facilitate meetings and workshops, create a ... Analytics reporting concepts eVars, Props, Processing Rules, Classifications, data layer, etc

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Telecom Data Analyst information

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How much do telecom data analyst jobs pay per year?

As of Jul 6, 2026, the average yearly pay for telecom data analyst in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Telecom Data Analyst position, and why are they important?

To thrive as a Telecom Data Analyst, you need a strong background in data analysis, statistical methods, and telecommunications concepts, often supported by a relevant degree in computer science, mathematics, or engineering. Familiarity with data analysis tools like SQL, Python, R, and telecom-specific platforms such as OSS/BSS systems, along with certifications such as Certified Data Analyst or relevant vendor credentials, is highly valuable. Effective communication, analytical thinking, and attention to detail are essential soft skills for collaborating with cross-functional teams and translating data insights into actionable strategies. These capabilities are critical for optimizing network performance, identifying trends, and supporting data-driven decision-making in the fast-evolving telecom industry.

What is a Telecom Data Analyst job?

A Telecom Data Analyst analyzes and interprets large datasets related to telecommunications networks, customer usage, and operational performance. They use data mining, statistical analysis, and visualization tools to identify trends, optimize network performance, and support business decisions. Their role may involve working with big data platforms, SQL, Python, or other analytics tools to extract insights. Additionally, they collaborate with engineers, business teams, and stakeholders to improve service efficiency and customer experience.

What does a typical day look like for a Telecom Data Analyst?

A typical day for a Telecom Data Analyst involves collecting and analyzing large sets of network data, identifying patterns or anomalies, and preparing detailed reports for technical and business teams. You might collaborate closely with engineers, IT specialists, and business analysts to improve network efficiency, solve issues, or support new projects. Many analysts also participate in regular meetings to discuss findings and recommend actionable changes based on data-driven insights. The role often includes monitoring key performance indicators (KPIs) and troubleshooting data discrepancies to ensure network reliability and customer satisfaction.

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Telecom Data Analyst jobs near you
Lead Data Scientist Propensity & Segmentation (Telecom)

Lead Data Scientist Propensity & Segmentation (Telecom)

Emergere Technologies

Irving, TX • On-site

Other

Posted 25 days ago

Job description

Position: Lead Data Scientist – Propensity & Segmentation (Telecom)
Location: Irving, TX (3 days hybrid onsite)
 
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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