Senior Data Scientist, Data Analytics

Calix

2d•Remote

About The Position

Calix is building decision intelligence capabilities across its business. The Analytics & Decision Science team develops predictive, causal, and simulation-driven solutions that influence how the company plans and operates. The Senior Data Scientist will build forecasting, simulation, and causal inference capabilities to evaluate business strategies, quantify uncertainty, and improve leadership decision-making, with a primary focus on supporting the Success business unit’s customer retention, expansion, adoption, and value-realization objectives. The role translates complex business systems into production-grade models and decision-support tools that deliver measurable business and customer outcomes. The position includes developing time-series forecasts, scenario and simulation frameworks, causal and probabilistic models, and analytical capabilities for Success leaders. The Senior Data Scientist manages models through their full lifecycle, partners with data engineering and platform teams to integrate models into enterprise systems and workflows, and communicates scenarios, insights, and recommendations to leadership. The role is expected to establish rigorous evaluation practices and build scalable capabilities that are adopted in operating workflows and produce measurable 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, to project outcomes and evaluate interdependencies across complex business systems.
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 where they are actively used to make business decisions.
Strong communication skills, including the ability to explain methods, limitations, uncertainty, and recommendations to business and executive stakeholders.

Nice To Haves

Experience in subscription, SaaS, telecommunications, customer success, or customer lifecycle analytics.
Familiarity with NRR, retention, churn, expansion, product adoption, customer health, and value-realization metrics.
Experience developing models for customer segmentation, risk identification, intervention prioritization, or expansion planning.
Experience with modern causal modeling techniques and libraries such as EconML, DoWhy, CausalML, PyMC, statsmodels, or equivalent tools.
Familiarity with model lifecycle and MLOps practices for deployment, reproducibility, monitoring, and governance.
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.

Responsibilities

Design and develop time-series forecasting models for metrics including 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 that combine forecasts, business constraints, and causal effects to evaluate strategies under uncertainty.
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 historical and current enterprise data to train, back-test, validate, and continuously improve models.
Manage the model lifecycle, including problem framing, feature engineering, model 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 measurement of customer and business impact.
Translate customer and business dynamics into scenarios, insights, and recommendations aligned with leadership decision points.