Senior Data Scientist, Data Analytics

01 Calix North America; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Calix is seeking a hands-on Senior Data Scientist to build forecasting, simulation, and causal inference capabilities that influence planning and operations. A primary focus is accelerating Success unit outcomes—retention, expansion, adoption, and value realization—by deploying production-grade models that inform leadership decisions.

The role emphasizes end-to-end model lifecycle, collaboration with data engineering, and rigorous evaluation to deliver measurable business impact across

Qualifications

• 7+ years of experience in data science or decision science in production.
• Advanced Python skills with pandas and NumPy.
• Deep SQL proficiency with large datasets (Snowflake or similar).
• Experience building production-grade time-series forecasting systems.
• Experience with simulation models, such as Monte Carlo methods.
• Hands-on causal inference, uplift modeling, or experimentation.
• Bayesian or probabilistic methods to quantify uncertainty.
• Translate complex problems into structured mathematical or statistical models.
• Integrate models into planning workflows and decision processes.

Responsibilities

• Design and develop robust time-series forecasting models for revenue, retention, churn, expansion, adoption, and demand.
• Partner with Success leaders to identify drivers of customer outcomes and analytical needs.
• Build simulation and scenario-modeling frameworks that consider forecasts, constraints, and causal effects.
• Apply causal inference, experimentation, and uplift modeling to isolate intervention effects.
• Develop Bayesian and probabilistic models to provide decision ranges.
• Train, back-test, validate, and continuously improve models with enterprise data.
• Manage end-to-end model lifecycle: framing, features, development, deployment, monitoring, tuning.
• Collaborate with data engineering to deploy scalable models in enterprise workflows.
• Establish rigorous evaluation practices and explainability for leadership insights.
• Translate dynamics into clear scenarios, insights, and actionable recommendations.

Skills

Python

Time-series forecasting

Causal inference

Experimentation

Communication with stakeholders

Education

Master's degree or PhD in a quantitative field

Tools

Snowflake

EconML

DoWhy

Job description

Calix is building the next generation of decision intelligence capabilities across its business. The Analytics & Decision Science team is responsible for moving beyond reporting to develop predictive, causal, and simulation-driven solutions that directly influence how the company plans and operates. We are seeking a hands‑on Senior Data Scientist to build the forecasting, simulation, and causal inference capabilities needed to evaluate business strategies, quantify uncertainty, and improve leadership decision‑making. A primary focus of this role will be supporting the Success business unit’s objectives, including improving customer retention, expansion, adoption, and value realization. This role will translate complex, real‑world business systems into production‑grade models and decision‑support tools that deliver measurable business and customer outcomes.

What You'll Do

• Design and develop robust time‑series forecasting models for critical business metrics, including revenue, retention, churn, expansion, customer adoption, and operational demand.
• Partner with Success leaders to identify the key drivers of customer outcomes and develop analytical capabilities that support the business unit’s strategic and operational objectives.
• Build simulation and scenario‑modeling frameworks that integrate forecasts, business constraints, and causal effects to evaluate strategies and support decisions under uncertainty.
• Apply causal inference, experimentation, and uplift modeling to isolate and measure the impact of customer and business interventions against baseline trends.
• Develop Bayesian and probabilistic models that quantify uncertainty and provide leaders with decision ranges rather than point estimates alone.
• Use historical and current enterprise data to train, back‑test, validate, and continuously improve models.
• Manage the end‑to‑end model lifecycle, including problem framing, feature engineering, model development, deployment, monitoring, and tuning.
• Partner with data engineering and platform teams to deploy scalable models that integrate with enterprise data systems and Success business workflows.
• Establish rigorous evaluation practices, including back‑testing, sensitivity analysis, explainability, and measurement of customer and business impact.
• Translate complex customer and business dynamics into clear scenarios, insights, and recommendations aligned to leadership decision points.

What We’re Looking For

• 7+ years of experience in data science, decision science, applied machine learning, statistical modeling, or advanced analytics in production environments.
• Advanced Python skills, including experience with pandas, NumPy, scikit‑learn, and relevant statistical modeling libraries.
• Deep SQL proficiency and experience working 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.
• Demonstrated 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.

Preferred Qualifications

• 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 that support 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.

What Success Looks Like

• Establish reliable, multi‑horizon forecasts for critical Success and business metrics.
• Provide Success leadership with scenario‑modeling capabilities used to evaluate customer strategies, investments, and resource decisions.
• Enable credible measurement of customer interventions against baseline retention, adoption, expansion, and value‑realization performance.
• Identify the leading indicators and causal drivers of customer outcomes so teams can take earlier and more targeted action.
• Integrate models into Success planning and operating workflows so analytical insights lead to measurable action.
• Build trusted, scalable capabilities that demonstrate sustained adoption and measurable customer and business impact.

#LI-Remote

The base pay range for this position varies based on the geographic location. More information about the pay range specific to candidate location and other factors will be shared during the recruitment process. Individual pay is determined based on location of residence and multiple factors, including job‑related knowledge, skills and experience. San Francisco Bay Area: Select US Metros and States: Other US Locations: As a part of the total compensation package, this role may be eligible for a bonus.

For information on our benefits click here.

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The Calix platform enables Communication Service Providers (CSPs) of all sizes to transform and future‑proof their businesses. Through real‑time data, automation, and actionable insights delivered via Calix One – our cloud‑first, AI‑powered platform – CSPs can simplify operations, collapse cost, and accelerate innovation. Calix One brings together the automation of everything and the experience of one, empowering customers to deliver differentiated subscriber experiences while driving acquisition, loyalty, and revenue growth. This is the Calix mission: to enable CSPs of all sizes to simplify, innovate, and grow, strengthening both their businesses and the communities they serve. We’re at the forefront of a once‑in‑a‑generation change in the broadband industry. Join us as we innovate, help our customers reach their potential, and connect underserved communities with unrivaled digital experiences.

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