Senior Data Scientist, Network Intelligence

# Senior Data Scientist, Network Intelligence Xplore Inc.

Full Time senior Airdrie, Alberta, CAPosted 2 days ago##

Role Overview Xplore Inc. is hiring a Senior Data Scientist, Network Intelligence. This is a full-time role in Airdrie. Part of Xplore Inc.'s Lifecycle hiring, posted 2 days ago. Full responsibilities, required qualifications, and the apply link are listed in the description below.## Salary Context Salary is not disclosed in this posting. Market median for Senior-level Lifecycle roles is $108k-$158k (based on 72 comparable listings).

Many employers share specifics during the interview process or after an initial screen.## Resume Keywords to Include Make sure these keywords appear in your resume to improve ATS scoring

Python Pandas Tensor Flow Py Torchscikit -learn

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Job Description Xplore Inc. is Canada's fibre, 5G and satellite broadband company for rural living. Xplore is committed to the relentless pursuit of an improved broadband experience for all Canadians. Xplore is building a world-class fibre optic and 5G wireless network to enable innovative broadband services for better every day rural living, for today and future generations.

Position Summary As a Senior Data Scientist, you will develop and deploy the predictive analytics and ML models that enable Xplore's network to become self-monitoring and increasingly self-optimizing across Fiber, Fixed Wireless, and Satellite platforms.

You will translate high-resolution network telemetry, operational event data, and customer experience signals into production ML models that reduce incident response times, improve capacity planning accuracy, and surface network degradation before customers are impacted. This role requires building and operating solutions across both cloud and on-premises environments-not only supporting migration initiatives, but also designing, developing, and deploying capabilities that run natively on-prem where needed.

All modeling work is built and served on Databricks, leveraging Gold-tier data assets and integrating with automation and observability pipelines operated by the broader network intelligence squad.

You will mentor data scientists on the team and serve as the technical ML authority for the Network Intelligence domain.

Key responsibilities include:

* Develop and deploy ML models for anomaly detection, predictive failure, capacity forecasting, and network performance degradation using Databricks and MLflow.
* Build feature engineering pipelines from network telemetry (RAN KPIs, alarms, traffic counters, customer experience data) using PySpark and Python against Gold-tier Databricks assets.
* Design and maintain MLflow experiment tracking, model registries, and retraining pipelines to support production model lifecycle management.
* Evaluate and apply appropriate algorithms matched to network time-series and event-driven data patterns: gradient boosting, LSTMs, isolation forest, survival models, and similar.
* Build optimization and scoring frameworks to prioritize network interventions: incident severity scoring, site risk tiering, maintenance urgency ranking.
* Integrate model outputs with automation pipelines so that insights translate into actionable triggers for network operations teams.
* Translate complex model outputs into clear narratives, visualizations, and Databricks AI/BI dashboard inputs consumable by engineering, operations, and executive audiences.
* Partner with Data Engineers to specify feature datasets, ensure data quality, and align on pipeline dependencies.
* Present findings and model performance to Director and VP-level stakeholders with confidence and clarity.

The ideal candidate will possess:
* 7-10+ years of applied data science or ML engineering experience in a production environment.
* Strong proficiency in Python for data science: pandas, scikit-learn, XGBoost, and PyTorch or Tensor Flow.
* Hands-on experience building and deploying data science or ML solutions across both cloud and on-premises environments, including Databricks for model development, MLflow for…