Senior Data Scientist

Xplore Inc.; Calgary; On-site; CAD 90,000 - 120,000; Full time

Job summary

A leading analytics company in Canada is seeking a Senior Data Scientist to drive measurable business outcomes through advanced modeling initiatives. You will lead projects focused on churn reduction, upselling, and network optimization. The ideal candidate has 5-7 years of experience in data science, strong statistical modeling skills, and proficiency in Python and cloud environments. You will work cross-functionally to provide insights that influence executive decisions and team strategies, while mentoring junior talent.

Qualifications

• 5–7+ years of experience in applied data science/machine learning.
• Strong foundation in statistical modeling and causal inference.
• Experience with large-scale modeling and distributed computing.
• Proven track record of delivering measurable business impact.

Responsibilities

• Lead complex modeling projects including churn modeling.
• Define modeling strategies and appropriate algorithms.
• Collaborate with cross-functional teams on data science solutions.
• Mentor junior/intermediate data scientists.

Skills

Statistical modeling

Causal inference

Time-series forecasting

Python

Spark

MLOps best practices

A/B testing

Communication

Leadership

Education

Master’s or PhD degree

Tools

Azure

Databricks

ArcGIS

PyTorch

Git

Salesforce

Job description

As a Senior Data Scientist at Xplore, you will drive measurable business outcomes by leading advanced modeling initiatives that directly support revenue growth, customer retention, and operational efficiency. Your work will enable smarter decision‑making across the organization, powering strategies for churn reduction, upsell targeting, and network optimization. Beyond technical leadership, you’ll help shape the data science roadmap to align with high‑priority business goals, mentor emerging talent, and deliver insights that translate into real commercial impact.

Key Responsibilities

• Lead complex modeling projects across technical and commercial domains, such as:
  • Churn propensity modeling and retention strategy design
  • Upsell and cross‑sell opportunity scoring
  • Product coverage estimation and e‑coverage optimization
  • Network infrastructure investment optimization & anomaly detection
• Define modeling strategies and select appropriate algorithms (deep learning, time‑series, probabilistic models, causal inference).
• Evaluate trade‑offs: accuracy vs latency vs interpretability, cost vs benefit, model maintenance and scalability.
• Collaborate with cross‑functional teams (sales, marketing, product, operations, care, network) to translate business needs into data science solutions that drive measurable value.
• Own and oversee model deployment, monitoring, drift detection, and retraining pipelines.
• Ensure governance around data ethics, fairness, and privacy, especially with customer data, usage logs, and location data.
• Mentor junior/intermediate data scientists; conduct code reviews and promote best practices (reproducibility, testing, documentation).
• Provide actionable insights to support executive decision‑making and quantify business impact (revenue uplift, churn reduction, campaign ROI).

The Ideal Candidate

• 5–7+ years of experience in applied data science / machine learning, ideally with exposure to sales and marketing domains.
• Strong foundation in statistical modeling, causal inference, time‑series forecasting, and uplift modeling to support customer insights and campaign effectiveness.
• Experience with A/B testing and experimentation frameworks to evaluate campaign effectiveness and optimize customer engagement strategies.
• Experience with large‑scale modeling and distributed computing in cloud environments.
• Strong command of Python, Spark, sophisticated machine learning frameworks, and MLOps best practices.
• Experience with Azure, Databricks, ArcGIS, PyTorch, and Git is considered advantageous.
• Experience with customer data protection, privacy regulations, and ethical AI principles.
• Familiarity with marketing platforms or CRM systems (e.g., Salesforce) to support targeted outreach, segmentation, and lifecycle analytics.
• Proven track record of delivering measurable business impact (improved retention, increased ARPU, optimized campaign performance).
• Excellent communication skills, including non‑technical stakeholder communication; strong leadership and mentoring capabilities.
• Master’s or PhD degree preferred; substantial industry experience may substitute for academic credentials.