Senior Data Analyst developing Power BI dashboards and governed data solutions for Michelin’s Beyond - Road mobility business. Supporting global teams with reporting, analytics, and data - driven decisions.
About the role
- Senior Data Scientist building forecasting, simulation, and causal models for Calix’s customer-success analytics. Translating enterprise data into decision tools for retention, adoption, expansion, and value realization.
Responsibilities
- Design and develop robust time-series forecasting models for revenue, retention, churn, expansion, customer adoption, and operational demand
- Partner with Success leaders to identify drivers of customer outcomes and develop analytical capabilities supporting strategic and operational objectives
- Build simulation and scenario-modeling frameworks integrating forecasts, business constraints, and causal effects
- Apply causal inference, experimentation, and uplift modeling to measure customer and business interventions against baseline trends
- Develop Bayesian and probabilistic models that quantify uncertainty and provide decision ranges
- Use enterprise data to train, back-test, validate, and continuously improve models
- Manage the end-to-end model lifecycle, including problem framing, feature engineering, development, deployment, monitoring, and tuning
- Partner with data engineering and platform teams to deploy scalable models integrated with enterprise data systems and Success workflows
- Establish evaluation practices including back-testing, sensitivity analysis, explainability, and impact measurement
- Translate customer and business dynamics into scenarios, insights, and recommendations aligned to leadership decision points
- Establish reliable multi-horizon forecasts for critical Success and business metrics
- Provide leadership with scenario-modeling capabilities for evaluating customer strategies, investments, and resource decisions
- Enable measurement of customer interventions against baseline retention, adoption, expansion, and value-realization performance
- Identify leading indicators and causal drivers of customer outcomes
- Integrate models into Success planning and operating workflows to drive measurable action
- Build trusted, scalable capabilities demonstrating sustained adoption and measurable customer and business impact
Requirements
- 7+ years of experience in data science, decision science, applied machine learning, statistical modeling, or advanced analytics in production environments
- Advanced Python skills, including pandas, NumPy, scikit-learn, and relevant statistical modeling libraries
- Deep SQL proficiency and experience with large, complex datasets on Snowflake or a comparable cloud data platform
- Extensive experience building production-grade time-series forecasting systems, including multi-horizon forecasting and rigorous back-testing
- Experience constructing simulation models, such as Monte Carlo models
- Hands-on experience measuring incrementality and treatment effects using experimentation, causal inference, observational methods, or uplift modeling
- Experience applying Bayesian or probabilistic methods to uncertainty estimation and decision-making
- Strong understanding of statistics, probability, experimental design, model explainability, and responsible modeling practices
- Ability to translate ambiguous business problems into structured mathematical or statistical models
- Experience integrating models into planning or operational workflows used to make business decisions
- Strong communication skills, including explaining methods, limitations, uncertainty, and recommendations to business and executive stakeholders
- Preferred: experience in subscription, SaaS, telecommunications, customer success, or customer lifecycle analytics
- Preferred: familiarity with NRR, retention, churn, expansion, product adoption, customer health, and value-realization metrics
- Preferred: experience developing models supporting customer segmentation, risk identification, intervention prioritization, or expansion planning
- Preferred: experience with EconML, DoWhy, CausalML, PyMC, statsmodels, or equivalent tools
- Preferred: familiarity with MLOps practices for deployment, reproducibility, monitoring, and governance
- Preferred: experience integrating analytical outputs into enterprise planning, reporting, and operational decision workflows
- Master’s degree or Ph.D. in Data Science, Statistics, Computer Science, Applied Mathematics, Operations Research, Engineering, or a related quantitative discipline; equivalent practical experience will be considered
Benefits
- Bonus eligibility as part of the total compensation package
- Benefits package (specific benefits linked in the posting)
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