Senior Data Scientist

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.